{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fantasy (first steps)\n",
    "This shows how to use Pandas to manipulate roster data.\n",
    "\n",
    "$\\rightarrow$ Use `[Control] + [Enter]` to evaluate a cell. (Check the 'Help' menu above for more.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##\n",
    "# Setup -- import the modules we want and set up inline plotting\n",
    "#\n",
    "from __future__ import print_function\n",
    "import datetime\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Bigger fonts and figures for the demo\n",
    "matplotlib.rcParams.update({\n",
    "        'font.size': 14,\n",
    "        'figure.figsize':(10.0, 8.0),\n",
    "        'axes.formatter.useoffset':False })\n",
    "\n",
    "# Better data frame display for the demo\n",
    "pd.set_option('expand_frame_repr', True)\n",
    "pd.set_option('max_rows', 18)\n",
    "pd.set_option('max_colwidth', 14)\n",
    "pd.set_option('precision',2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First we need to load the data\n",
    "\n",
    "It's the total 2014 stats for people who started in NFL games last year. Thanks to [TeamRankings.com][teamrankings]. We're missing the count of safeties but hopefully they're so infrequent they don't affect rankings.\n",
    "\n",
    "[teamrankings]: https://www.teamrankings.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Team', u'Player', u'pos', u'starts', u'fumblesLost', u'twoPt',\n",
       "       u'passingATT', u'passingCOMP', u'passingINT', u'passingTD',\n",
       "       u'passingYDS', u'receivingREC', u'receivingTD', u'receivingYDS',\n",
       "       u'rushingATT', u'rushingTD', u'rushingYDS', u'kicking_extraPt',\n",
       "       u'kicking_FGge50A', u'kicking_FGge50M', u'kicking_FGlt50A',\n",
       "       u'kicking_FGlt50M', u'defenseF', u'defenseSCK', u'defenseTOT',\n",
       "       u'defenseFumblesRecovered', u'fumblesRecoveredTD', u'pointreturnsFC',\n",
       "       u'pointreturnsRETURNS', u'pointreturnsTD', u'interceptionsINT',\n",
       "       u'interceptionsTD', u'interceptionsYDS', u'kickreturnsRETURNS',\n",
       "       u'kickreturnsTD', u'kickreturnsYDS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##\n",
    "# Player data and the season schedule (for byes)\n",
    "import os\n",
    "\n",
    "player_stats_file = os.path.join('data', 'nfl_player_stats_season2014.csv')\n",
    "season2015_file = os.path.join('data', 'nfl_season2015.csv')\n",
    "prior_seasons_file = os.path.join('data', 'nfl_season2008to2014.csv')\n",
    "\n",
    "stats = pd.read_csv(player_stats_file)\n",
    "season2015 = pd.read_csv(season2015_file)\n",
    "prior_seasons = pd.read_csv(prior_seasons_file)  # Don't delete this, we need it later\n",
    "\n",
    "stats.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column definitions\n",
    "\n",
    "- `Team` - the full team name, exactly the same as in the other datasets\n",
    "- `player` - the player name, plus team totals (`TOTAL`) and opponent totals (`OPPONENT TOTAL`)\n",
    "- `pos` - abbreviation for positions, such as `QB`=quarterback, `K`=kicker\n",
    "- `starts` - number of starts in the 2014 season\n",
    "- `fumblesLost` - fumbles lost to the opposing team in 2014\n",
    "- `fumblesRecoveredTD` - fumbles recovered for a touchdown in 2014\n",
    "- `twoPt` - two point conversion\n",
    "- **Passing**\n",
    "    + `passingATT` - attempted passes in 2014\n",
    "    + `passingCOMP` - completed passes in 2014\n",
    "    + `passingINT` - intercepted passes in 2014\n",
    "    + `passingTD` - passing touchdowns in 2014\n",
    "    + `passingYDS` - passing yards in 2014\n",
    "- **Receiving**\n",
    "    + `receivingREC` - receptions in 2014\n",
    "    + `receivingTD` - touchdowns made off of a reception in 2014\n",
    "    + `receivingYDS` - receiving yards in 2014\n",
    "- **Rushing**\n",
    "    + `rushingATT` - rushing attempts in 2014\n",
    "    + `rushingTD` - rushing touchdowns in 2014\n",
    "    + `rushingYDS` - rushing yards in 2014\n",
    "- **Kicking**\n",
    "    + `kicking_extraPt` - extra points made in 2014\n",
    "    + `kicking_FGge50A` - field golds $\\ge$ 50 yards attempted\n",
    "    + `kicking_FGge50M` - field golds $\\ge$ 50 yards made\n",
    "    + `kicking_FGlt50A` - field golds $\\lt$ 50 yards attempted\n",
    "    + `kicking_FGlt50M` - field golds $\\lt$ yards made\n",
    "- **Defense**\n",
    "    + `defenseF` - fumbles forced in 2014\n",
    "    + `defenseSCK` - sacks in 2014\n",
    "    + `defenseTOTAL` - tackles in 2014\n",
    "    + `defenseFumblesRecovered` - fumbles recovered in 2014\n",
    "    + `pointreturnsFC` - fair catches on point returns in 2014\n",
    "    + `pointreturnsRETURNS` - returns made on point returns in 2014\n",
    "    + `pointreturnsTD` - point returns for a touchdown in 2014\n",
    "    + `interceptionsINT` - interceptions in 2014\n",
    "    + `interceptionsTD` - interceptions for a touchdown in 2014\n",
    "    + `interceptionsYDS` - yards gained on interceptions in 2014\n",
    "    + `kickreturnsRETURNS` - kick returns in 2014\n",
    "    + `kickreturnsTD` - kick returns returned for a touchdown in 2014\n",
    "    + `kickreturnsYDS` - yards gained during kick returns in 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate the fumble recovery data for the defense\n",
    "\n",
    "In Fantasy Football, you choose a defensive team, not individual players.\n",
    "\n",
    "The dataset we have has rows in the column `player` as 'TOTAL' and 'OPPONENT TOTAL' for total defensive stats, except for safeties *(which I couldn't get easily - but which also are so rare that they shoudn't affect rankings too badly)* and fumbles recovered for a touchdown (`fumblesRecoveredTD`) which were from a separate dataset and not added in.\n",
    "\n",
    "We need to aggregate `fumblesRecoveredTD` over the individual players to get a score for the defense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalFumblesRecoveredTD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       totalFumblesRecoveredTD\n",
       "count           21.0          \n",
       "mean             1.2          \n",
       "std              0.5          \n",
       "min              1.0          \n",
       "25%              1.0          \n",
       "50%              1.0          \n",
       "75%              1.0          \n",
       "max              3.0          "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byteam_fumbleRecoveries = stats.groupby('Team').fumblesRecoveredTD.agg({'totalFumblesRecoveredTD': 'sum'}).reset_index()\n",
    "byteam_fumbleRecoveries.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add the fumble recoveries to the overall defensive team's stats\n",
    "The overall stats are in rows for each team with the player name as `TOTAL`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Team</th>\n",
       "      <th>interceptionsINT</th>\n",
       "      <th>interceptionsTD</th>\n",
       "      <th>pointreturnsTD</th>\n",
       "      <th>kickreturnsTD</th>\n",
       "      <th>fumblesRecoveredTD</th>\n",
       "      <th>defenseSCK</th>\n",
       "      <th>defenseF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Minnesota ...</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Cleveland ...</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Jacksonvil...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>New York Jets</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Arizona Ca...</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>New York G...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Baltimore ...</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Philadelph...</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Denver Bro...</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Carolina P...</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Atlanta Fa...</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Buffalo Bills</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Dallas Cow...</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>San Diego ...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Seattle Se...</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>TOTAL</td>\n",
       "      <td>New Englan...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player           Team  interceptionsINT  interceptionsTD  pointreturnsTD  \\\n",
       "18    TOTAL  Houston Te...              3                 1                0    \n",
       "78    TOTAL  Green Bay ...             18                 3                2    \n",
       "127   TOTAL  Minnesota ...             13                 2                0    \n",
       "184   TOTAL  Cleveland ...             21                 2                0    \n",
       "234   TOTAL  Jacksonvil...              6                 1                0    \n",
       "290   TOTAL  New York Jets              6                 0                0    \n",
       "349   TOTAL  Arizona Ca...             18                 3                1    \n",
       "433   TOTAL  New York G...             17                 0                0    \n",
       "464   TOTAL  Baltimore ...             11                 1                0    \n",
       "...     ...            ...            ...               ...              ...    \n",
       "1326  TOTAL  Philadelph...             12                 2                2    \n",
       "1372  TOTAL  Denver Bro...             18                 2                0    \n",
       "1427  TOTAL  Carolina P...             14                 2                1    \n",
       "1483  TOTAL  Atlanta Fa...             16                 1                1    \n",
       "1541  TOTAL  Buffalo Bills              3                 1                0    \n",
       "1626  TOTAL  Dallas Cow...             18                 2              NaN    \n",
       "1649  TOTAL  San Diego ...              7                 0              NaN    \n",
       "1707  TOTAL  Seattle Se...             13                 2              NaN    \n",
       "1756  TOTAL  New Englan...             16                 0              NaN    \n",
       "\n",
       "      kickreturnsTD  fumblesRecoveredTD  defenseSCK  defenseF  \n",
       "18                0            NaN                9         1  \n",
       "78                0            NaN               41        12  \n",
       "127               0            NaN               41         9  \n",
       "184               0            NaN               31        15  \n",
       "234               0            NaN               45        19  \n",
       "290               0            NaN               45         9  \n",
       "349               0            NaN               35         9  \n",
       "433               0            NaN               47        15  \n",
       "464               1            NaN               49        16  \n",
       "...             ...            ...              ...       ...  \n",
       "1326              2            NaN               49        24  \n",
       "1372              0            NaN               41        15  \n",
       "1427              0            NaN               40        22  \n",
       "1483              0            NaN               22        13  \n",
       "1541              0            NaN                8         1  \n",
       "1626            NaN            NaN               28        28  \n",
       "1649            NaN            NaN               26        26  \n",
       "1707            NaN            NaN               37        37  \n",
       "1756            NaN            NaN               40        40  \n",
       "\n",
       "[32 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[stats.Player == 'TOTAL'][[\n",
    "        'Player', 'Team',\n",
    "        'interceptionsINT', 'interceptionsTD',\n",
    "        'pointreturnsTD', 'kickreturnsTD', 'fumblesRecoveredTD',\n",
    "        'defenseSCK', 'defenseF']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge the `byteam_fumbleRecoveries` stats back into the original stats frame\n",
    "1. Add a false player name 'TOTAL' to the `byteam_fumbleRecoveries` data frame. We will merge on that name plus the team to select the correct row.\n",
    "2. Merge `byteam_fumbleRecoveries` back into the `stats` data frame, matching on the same team name (and with `player` as the `TOTAL`).\n",
    "3. Coalesce the aggregated column and the original column together\n",
    "4. Delete the column `totalFumblesRecoveredTD` (which we made for the aggregation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>passingTD</th>\n",
       "      <th>receivingTD</th>\n",
       "      <th>rushingTD</th>\n",
       "      <th>fumblesRecoveredTD</th>\n",
       "      <th>pointreturnsTD</th>\n",
       "      <th>interceptionsTD</th>\n",
       "      <th>kickreturnsTD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Minnesota ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Cleveland ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Jacksonvil...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>New York Jets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Arizona Ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>New York G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Baltimore ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>Philadelph...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>Denver Bro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>Carolina P...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>Atlanta Fa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>Buffalo Bills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>Dallas Cow...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>San Diego ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>Seattle Se...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>New Englan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Team  passingTD  receivingTD  rushingTD  fumblesRecoveredTD  \\\n",
       "18    Houston Te...        NaN          NaN        NaN              1        \n",
       "78    Green Bay ...        NaN          NaN        NaN              1        \n",
       "127   Minnesota ...        NaN          NaN        NaN              1        \n",
       "184   Cleveland ...        NaN          NaN        NaN              1        \n",
       "234   Jacksonvil...        NaN          NaN        NaN              2        \n",
       "290   New York Jets        NaN          NaN        NaN            NaN        \n",
       "349   Arizona Ca...        NaN          NaN        NaN              1        \n",
       "433   New York G...        NaN          NaN        NaN              1        \n",
       "464   Baltimore ...        NaN          NaN        NaN            NaN        \n",
       "...             ...        ...          ...        ...            ...        \n",
       "1326  Philadelph...        NaN          NaN        NaN              2        \n",
       "1372  Denver Bro...        NaN          NaN        NaN              1        \n",
       "1427  Carolina P...        NaN          NaN        NaN            NaN        \n",
       "1483  Atlanta Fa...        NaN          NaN        NaN              1        \n",
       "1541  Buffalo Bills        NaN          NaN        NaN              1        \n",
       "1626  Dallas Cow...        NaN          NaN        NaN              1        \n",
       "1649  San Diego ...        NaN          NaN        NaN              3        \n",
       "1707  Seattle Se...        NaN          NaN        NaN            NaN        \n",
       "1756  New Englan...        NaN          NaN        NaN              2        \n",
       "\n",
       "      pointreturnsTD  interceptionsTD  kickreturnsTD  \n",
       "18                0               1                0  \n",
       "78                2               3                0  \n",
       "127               0               2                0  \n",
       "184               0               2                0  \n",
       "234               0               1                0  \n",
       "290               0               0                0  \n",
       "349               1               3                0  \n",
       "433               0               0                0  \n",
       "464               0               1                1  \n",
       "...             ...             ...              ...  \n",
       "1326              2               2                2  \n",
       "1372              0               2                0  \n",
       "1427              1               2                0  \n",
       "1483              1               1                0  \n",
       "1541              0               1                0  \n",
       "1626            NaN               2              NaN  \n",
       "1649            NaN               0              NaN  \n",
       "1707            NaN               2              NaN  \n",
       "1756            NaN               0              NaN  \n",
       "\n",
       "[32 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Add the 'player' column with value 'TOTAL'\n",
    "byteam_fumbleRecoveries['Player'] = 'TOTAL'\n",
    "\n",
    "# 2. Merge (left join) to add the 'fumblesRecoveredTD' values to the 'stats' data frame.\n",
    "stats = stats.merge(byteam_fumbleRecoveries, on=['Team', 'Player'], how='left')\n",
    "\n",
    "# 3. Coalesce the two columns together into the original column\n",
    "#    (when I am assigning to a subset of rows I have to use the '.ix' accessor.\n",
    "#     The other one -- just [] -- will return a copy of the column and so\n",
    "#     the assignment will take place on the copy, not the original.)\n",
    "stats.ix[stats.fumblesRecoveredTD.isnull(), 'fumblesRecoveredTD'] = stats[\n",
    "    stats.fumblesRecoveredTD.isnull()].totalFumblesRecoveredTD\n",
    "\n",
    "# 4. Delete the column totalFumblesRecoveredTD.\n",
    "del stats['totalFumblesRecoveredTD']\n",
    "\n",
    "# Show the result\n",
    "stats[stats.Player=='TOTAL'][['Team'] + [c for c in stats.columns if c.endswith('TD')]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select only the players we want\n",
    "For standard fantasy, you get 9 players on your starting roster and 6 players on the bench.\n",
    "The defensive players are rolled into one (we just did that for the last relevant type of scoring play) and we don't care about the other positions.\n",
    "\n",
    "The parenthesized abbreviations are the corresponding value in the `pos` column in this dataset:\n",
    "\n",
    "- Quarterback (`QB`):1\n",
    "- Running Back (`RB`):2\n",
    "- Wide Receiver (`WR`):2\n",
    "- Tight End (`TE`):1\n",
    "- Wide Receiver / Running Back:1\n",
    "- Kicker (`K`):1\n",
    "- Defensive Team:1\n",
    "- Bench:6\n",
    "\n",
    "First, let's add a value `'DEFENSE'` as the position when the player is named `'TOTAL'`. Then we'll delete all of the players who aren't the kinds we can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'CB', 'K', 'RB', 'LB', 'DT', 'C', 'WR', 'DE', 'T', 'QB', 'TE',\n",
       "       'S', 'PK', 'FB', 'DB', 'P', 'G', 'NT'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the positions\n",
    "stats.pos.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(194, 36)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>pos</th>\n",
       "      <th>starts</th>\n",
       "      <th>fumblesLost</th>\n",
       "      <th>twoPt</th>\n",
       "      <th>passingATT</th>\n",
       "      <th>passingCOMP</th>\n",
       "      <th>passingINT</th>\n",
       "      <th>passingTD</th>\n",
       "      <th>...</th>\n",
       "      <th>fumblesRecoveredTD</th>\n",
       "      <th>pointreturnsFC</th>\n",
       "      <th>pointreturnsRETURNS</th>\n",
       "      <th>pointreturnsTD</th>\n",
       "      <th>interceptionsINT</th>\n",
       "      <th>interceptionsTD</th>\n",
       "      <th>interceptionsYDS</th>\n",
       "      <th>kickreturnsRETURNS</th>\n",
       "      <th>kickreturnsTD</th>\n",
       "      <th>kickreturnsYDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>DEFENSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>Randy Bullock</td>\n",
       "      <td>K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>Eddie Lacy</td>\n",
       "      <td>RB</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>DEFENSE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>396</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>Randall Cobb</td>\n",
       "      <td>WR</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>Mason Crosby</td>\n",
       "      <td>K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team         Player      pos  starts  fumblesLost  twoPt  \\\n",
       "18  Houston Te...          TOTAL  DEFENSE     NaN          NaN    NaN   \n",
       "31  Houston Te...  Randy Bullock        K     NaN          NaN    NaN   \n",
       "68  Green Bay ...     Eddie Lacy       RB      18            2    NaN   \n",
       "78  Green Bay ...          TOTAL  DEFENSE     NaN          NaN    NaN   \n",
       "89  Green Bay ...   Randall Cobb       WR      18            2      1   \n",
       "95  Green Bay ...   Mason Crosby        K     NaN          NaN    NaN   \n",
       "\n",
       "    passingATT  passingCOMP  passingINT  passingTD       ...        \\\n",
       "18         NaN          NaN         NaN        NaN       ...         \n",
       "31         NaN          NaN         NaN        NaN       ...         \n",
       "68         NaN          NaN         NaN        NaN       ...         \n",
       "78         NaN          NaN         NaN        NaN       ...         \n",
       "89         NaN          NaN         NaN        NaN       ...         \n",
       "95         NaN          NaN         NaN        NaN       ...         \n",
       "\n",
       "    fumblesRecoveredTD  pointreturnsFC  pointreturnsRETURNS  pointreturnsTD  \\\n",
       "18              1                   5              13                    0    \n",
       "31            NaN                 NaN             NaN                  NaN    \n",
       "68            NaN                 NaN             NaN                  NaN    \n",
       "78              1                  12              29                    2    \n",
       "89            NaN                   9              14                    0    \n",
       "95            NaN                 NaN             NaN                  NaN    \n",
       "\n",
       "    interceptionsINT  interceptionsTD  interceptionsYDS  kickreturnsRETURNS  \\\n",
       "18              3                 1               69                 6        \n",
       "31            NaN               NaN              NaN               NaN        \n",
       "68            NaN               NaN              NaN               NaN        \n",
       "78             18                 3              396                29        \n",
       "89            NaN               NaN              NaN                 1        \n",
       "95            NaN               NaN              NaN               NaN        \n",
       "\n",
       "    kickreturnsTD  kickreturnsYDS  \n",
       "18              0            116   \n",
       "31            NaN            NaN   \n",
       "68            NaN            NaN   \n",
       "78              0            555   \n",
       "89              0              0   \n",
       "95            NaN            NaN   \n",
       "\n",
       "[6 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Add a value 'DEFENSE' as the position when the player is named 'TOTAL'\n",
    "stats.ix[stats.Player == 'TOTAL', 'pos'] = 'DEFENSE'\n",
    "\n",
    "# 2. Delete players that aren't the types used in Fantasy\n",
    "stats = stats[stats.pos.isin(('QB', 'RB', 'WR', 'TE', 'K', 'DEFENSE'))]\n",
    "\n",
    "print(stats.shape)\n",
    "\n",
    "stats.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ranking the players\n",
    "...finally!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fantasy scoring\n",
    "\n",
    "The scoring shown in the table below uses the [NFL fantasy standard rules][nfl_standard]. The `fixed width` denotes the corresponding column name in the dataset.\n",
    "\n",
    "\n",
    "<table style=\"font-size:70%;\">\n",
    "<tr><th>Offense</th><th>Kicking</th><th>Defense</th>\n",
    "</tr><td style=\"padding:0;vertical-align:top;\">\n",
    "<ul style=\"padding-left:1;\">\n",
    "<li>Passing\n",
    " <ul style=\"margin-top:0;padding-left:-1;\">\n",
    " <li>Yards: +1 / 25 yds <br/> `passingYDS`\n",
    " <li>Touchdowns: +4 <br/> `passingTD`\n",
    " <li>Interceptions: -2 <br/> `passingINT`\n",
    "</ul>\n",
    "<li>Rushing\n",
    " <ul style=\"margin-top:0;padding-left:-1;\">\n",
    " <li>Yards: +1 / 10 yds <br/> `rushingYDS`\n",
    " <li>Touchdowns: +6 <br/> `rushingTD`\n",
    "</ul>\n",
    "<li>Receiving\n",
    " <ul style=\"margin-top:0;padding-left:-1;\">\n",
    " <li>Yards: +1 / 10 yds <br/> `receivingYDS`\n",
    " <li>Touchdowns: +6 <br/> `receivingTD`\n",
    "</ul>\n",
    "<li>Fumbles recovered<br/>for Touchdown: +6 `fumblesRecoveredTD`\n",
    "<li>2-Point Conversions: +2 <br/> `twoPt`\n",
    "<li>Fumbles Lost: -2 <br/> `fumblesLost`\n",
    "</ul>\n",
    "</td><td style=\"padding:0;vertical-align:top;\">\n",
    "Point Attempts Made\n",
    "<ul style=\"margin-top:0;padding-left:-1;\"><li>+1  `kicking_extraPt`</ul>\n",
    "Field Goals Made\n",
    " <ul style=\"margin-top:0;padding-left:-1;\">\n",
    " <li>0-49 yds: +3 <br/> `kicking_FGlt50M`\n",
    " <li>50+ yds: +5 <br/> `kicking_FGge50M`\n",
    " </ul>\n",
    "</td><td style=\"padding:0;vertical-align:top;\">\n",
    "<ul>\n",
    "<li>Sacks: +1 <br/> `defenseSCK`\n",
    "<li>Interceptions: +2 <br/> `interceptionsINT`\n",
    "<li>Fumbles Recovered: +2 <br/> `defenseF`\n",
    "<li>Safeties: +2 <br/> **no data**\n",
    "<li>Defensive Touchdowns: +6 <br/>  `interceptionsTD` and the `fumblesRecoveredTD` attributed to defensive players\n",
    "<li>Kick / Punt Return Touchdowns: +6 <br/> `kickreturnsTD` and `pointreturnsTD`\n",
    "<li>Points Allowed<br/>\n",
    " (get by aggregating the 2014 data from `nfl_season2008to2014.csv`)\n",
    " <ul>\n",
    " <li>(0): +10\n",
    " <li>(1-6): +7\n",
    " <li>(7-13): +4\n",
    " <li>(14-20): +1\n",
    " <li>(21-27): +0\n",
    " <li>(28-34): -1\n",
    " <li>(35+): -4\n",
    " </ul>\n",
    "</ul>\n",
    "</td><tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "[nfl_standard]: http://www.nfl.com/fantasyfootball/help/nfl-scoringsettings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the fantasy points for 'Points Allowed'\n",
    "\n",
    "We have the game-by-game data from the 2014 season in the dataset `prior_seasons`. Since all we have is aggregates everywhere else, there's no shame in just aggregating here too.\n",
    "\n",
    "1. $\\rightarrow$ Assign each game from 2014 in the `prior_seasons` data frame its respective Fantasy points according to the chart for Points Allowed, and sum them. This will give us one row per team.\n",
    "2. Merge it into the main `stats` data frame the same way as we merged `fumblesRecoveredTD`\n",
    "    1. Add the `player` column with value `TOTAL`\n",
    "    2. Merge (left join) to add the 'fantasyPA' values to the 'stats' data frame.\n",
    "    3. That's it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>fantasyPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Minnesota ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cleveland ...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jacksonvil...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team  fantasyPA\n",
       "0   Houston Te...         21\n",
       "3   Green Bay ...          5\n",
       "10  Minnesota ...         13\n",
       "14  Cleveland ...         15\n",
       "17  Jacksonvil...          2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0. Function that will be used in (1) to assign points to each game in a season\n",
    "def fantasy_points(points_allowed):\n",
    "    \"\"\"Calculate the fantasy score for the season, given the points allowed.\n",
    "\n",
    "    Points:     (0)  (1-6) (7-13) (14-20) (21-27) (28-34) (35+)\n",
    "                +10    +7     +4      +1      +0      -1    -4\n",
    "    \"\"\"\n",
    "    points = 10 * (points_allowed == 0).sum()\n",
    "    points += 7 * points_allowed.between(1,6).sum()\n",
    "    points += 4 * points_allowed.between(7,13).sum()\n",
    "    points += 1 * points_allowed.between(14,20).sum()\n",
    "    points += 0 * points_allowed.between(21,27).sum()\n",
    "    points += -1 * points_allowed.between(28,34).sum()\n",
    "    points += -4 * (points_allowed >= 35).sum()\n",
    "    return points\n",
    "\n",
    "# 1. Assign each game from 2014 in the `prior_seasons` data frame its respective Fantasy points\n",
    "defense_fantasy_points = prior_seasons[(prior_seasons.Season == 2014) & (prior_seasons.Week <= 17)\n",
    "             ].groupby('Team').PointsAllowed.agg({'fantasyPA' :fantasy_points})\n",
    "\n",
    "# reset the index (the groupby() made the 'Team' column into the index...get it out)\n",
    "defense_fantasy_points.reset_index(inplace=True)\n",
    "\n",
    "# 2 Merge it into the main 'stats' data frame\n",
    "#   2a -- Add the 'player' column with value 'TOTAL'\n",
    "defense_fantasy_points['Player'] = 'TOTAL'\n",
    "\n",
    "#   2b -- Merge the 'defense_fantasy_points' into the 'stats' data frame.\n",
    "#         This will add the column 'fantasyPA' to 'stats'\n",
    "if 'fantasyPA' in stats.columns:\n",
    "    del stats['fantasyPA']  # in case people run this cell more than once\n",
    "\n",
    "stats = stats.merge(defense_fantasy_points, on=['Team', 'Player'], how='left')\n",
    "\n",
    "stats[stats.fantasyPA.notnull()][['Team', 'fantasyPA']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimate the score:\n",
    "Approach:\n",
    "\n",
    "1. Fill all of the null entries with zero\n",
    "2. Add `FantasyPtsTotal` to the dataset, set to zero\n",
    "3. Go through each column separately and add the correct points from each one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>pos</th>\n",
       "      <th>starts</th>\n",
       "      <th>fumblesLost</th>\n",
       "      <th>twoPt</th>\n",
       "      <th>passingATT</th>\n",
       "      <th>passingCOMP</th>\n",
       "      <th>passingINT</th>\n",
       "      <th>passingTD</th>\n",
       "      <th>...</th>\n",
       "      <th>pointreturnsRETURNS</th>\n",
       "      <th>pointreturnsTD</th>\n",
       "      <th>interceptionsINT</th>\n",
       "      <th>interceptionsTD</th>\n",
       "      <th>interceptionsYDS</th>\n",
       "      <th>kickreturnsRETURNS</th>\n",
       "      <th>kickreturnsTD</th>\n",
       "      <th>kickreturnsYDS</th>\n",
       "      <th>fantasyPA</th>\n",
       "      <th>FantasyPtsTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>DEFENSE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>21</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Houston Te...</td>\n",
       "      <td>Randy Bullock</td>\n",
       "      <td>K</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>Eddie Lacy</td>\n",
       "      <td>RB</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Team         Player      pos  starts  fumblesLost  twoPt  \\\n",
       "0  Houston Te...          TOTAL  DEFENSE       0            0      0   \n",
       "1  Houston Te...  Randy Bullock        K       0            0      0   \n",
       "2  Green Bay ...     Eddie Lacy       RB      18            2      0   \n",
       "\n",
       "   passingATT  passingCOMP  passingINT  passingTD       ...         \\\n",
       "0           0            0           0          0       ...          \n",
       "1           0            0           0          0       ...          \n",
       "2           0            0           0          0       ...          \n",
       "\n",
       "   pointreturnsRETURNS  pointreturnsTD  interceptionsINT  interceptionsTD  \\\n",
       "0             13                    0               3                 1     \n",
       "1              0                    0               0                 0     \n",
       "2              0                    0               0                 0     \n",
       "\n",
       "   interceptionsYDS  kickreturnsRETURNS  kickreturnsTD  kickreturnsYDS  \\\n",
       "0             69                 6                   0            116    \n",
       "1              0                 0                   0              0    \n",
       "2              0                 0                   0              0    \n",
       "\n",
       "   fantasyPA  FantasyPtsTotal  \n",
       "0         21             71    \n",
       "1          0             55    \n",
       "2          0             90    \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Fill all of the null entries with zero\n",
    "stats = stats.fillna(0)\n",
    "\n",
    "# 2. Add `FantasyPtsTotal` to the dataset, set to zero\n",
    "stats['FantasyPtsTotal'] = 0\n",
    "\n",
    "# 3. Go through each column separately and add the correct points from each one\n",
    "\n",
    "### --------------------------------------------- Passing ----- ###\n",
    "##   passingYDS  -- Yards: +1 / 25 yds \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 1 * stats.passingYDS % 25\n",
    "\n",
    "##   passingTD  -- Touchdowns: +4 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 4 * stats.passingTD\n",
    " \n",
    "##   passingINT  -- Interceptions: -2\n",
    "stats.ix[:, 'FantasyPtsTotal'] -= 2 * stats.passingINT\n",
    "\n",
    "\n",
    "### --------------------------------------------- Rushing ----- ###\n",
    "##   rushingYDS  -- Yards: +1 / 10 yds \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 1 * stats.rushingYDS % 10\n",
    "\n",
    "##   rushingTD  -- Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.rushingTD\n",
    "\n",
    "\n",
    "### ------------------------------------------- Receiving ----- ###\n",
    "##   receivingYDS  -- Yards: +1 / 10 yds  \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 1 * stats.receivingYDS % 10\n",
    "\n",
    "##   receivingTD  -- Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.receivingTD\n",
    "\n",
    "\n",
    "### --------------------------------------------- General ----- ###\n",
    "##   fumblesRecoveredTD  -- Fumbles recovered for Touchdown: +6\n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.fumblesRecoveredTD\n",
    "\n",
    "##   twoPt  -- 2-Point Conversions: +2 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 2 * stats.twoPt\n",
    "\n",
    "##   fumblesLost  -- Fumbles Lost: -2 \n",
    "stats.ix[:, 'FantasyPtsTotal'] -= 2 * stats.fumblesLost\n",
    "\n",
    "\n",
    "### --------------------------------------------- Kicking ----- ###\n",
    "##   kicking_extraPt  -- Point Attempts Made: +1\n",
    "stats.ix[:, 'FantasyPtsTotal'] += 1 * stats.kicking_extraPt\n",
    "\n",
    "##   kicking_FGlt50M  -- Field Goals made at 0-49 yds: +3 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 3 * stats.kicking_FGlt50M\n",
    "\n",
    "##   kicking_FGge50M  -- Field Goals made at 50+ yds: +5 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 3 * stats.kicking_FGge50M\n",
    "\n",
    "\n",
    "### --------------------------------------------- Defense ----- ###\n",
    "##   defenseSCK  -- Sacks: +1 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 1 * stats.defenseSCK\n",
    "\n",
    "##   interceptionsINT  -- Interceptions: +2 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 2 * stats.interceptionsINT\n",
    "\n",
    "##   defenseF  -- Fumbles Recovered: +2 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 2 * stats.defenseF\n",
    "\n",
    "##   interceptionsTD -- Defensive Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.interceptionsTD\n",
    "\n",
    "##   fumblesRecoveredTD -- Defensive Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.fumblesRecoveredTD\n",
    "\n",
    "##   kickreturnsTD -- Defensive Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.kickreturnsTD\n",
    "\n",
    "##   pointreturnsTD -- Defensive Touchdowns: +6 \n",
    "stats.ix[:, 'FantasyPtsTotal'] += 6 * stats.pointreturnsTD\n",
    "\n",
    "##  ............................ Defense - Points Allowed ..... ###\n",
    "stats.ix[:, 'FantasyPtsTotal'] += stats.fantasyPA\n",
    "\n",
    "stats.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# -------------------------------------------------- DEFENSE ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "282\tPhiladelphia Eagles \tTOTAL\n",
      "231\tIndianapolis Colts  \tTOTAL\n",
      "231\tArizona Cardinals   \tTOTAL\n",
      "230\tBaltimore Ravens    \tTOTAL\n",
      "229\tGreen Bay Packers   \tTOTAL\n",
      "226\tSt. Louis Rams      \tTOTAL\n",
      "221\tCarolina Panthers   \tTOTAL\n",
      "217\tCleveland Browns    \tTOTAL\n",
      "215\tDetroit Lions       \tTOTAL\n",
      "214\tPittsburgh Steelers \tTOTAL\n",
      "# -------------------------------------------------- K ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "146\tIndianapolis Colts  \tAdam Vinatieri\n",
      "137\tGreen Bay Packers   \tMason Crosby\n",
      "136\tBaltimore Ravens    \tJustin Tucker\n",
      "132\tPittsburgh Steelers \tShaun Suisham\n",
      "128\tMiami Dolphins      \tCaleb Sturgis\n",
      "127\tAtlanta Falcons     \tMatt Bryant\n",
      "126\tCarolina Panthers   \tGraham Gano\n",
      "119\tNew York Jets       \tNick Folk\n",
      "118\tCincinnati Bengals  \tMike Nugent\n",
      "116\tNew York Giants     \tJosh Brown\n",
      "# -------------------------------------------------- QB ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "165\tGreen Bay Packers   \tAaron Rodgers\n",
      "148\tIndianapolis Colts  \tAndrew Luck\n",
      "132\tDenver Broncos      \tPeyton Manning\n",
      "124\tNew England Patriots\tTom Brady\n",
      "120\tSeattle Seahawks    \tRussell Wilson\n",
      "118\tDallas Cowboys      \tTony Romo\n",
      "115\tPittsburgh Steelers \tBen Roethlisberger\n",
      "113\tMiami Dolphins      \tRyan Tannehill\n",
      "109\tNew Orleans Saints  \tDrew Brees\n",
      "108\tCincinnati Bengals  \tAndy Dalton\n",
      "# -------------------------------------------------- RB ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "111\tSeattle Seahawks    \tMarshawn Lynch\n",
      "90\tGreen Bay Packers   \tEddie Lacy\n",
      "82\tKansas City Chiefs  \tJamaal Charles\n",
      "77\tDallas Cowboys      \tDeMarco Murray\n",
      "76\tChicago Bears       \tMatt Forte\n",
      "66\tMinnesota Vikings   \tMatt Asiata\n",
      "62\tMiami Dolphins      \tLamar Miller\n",
      "59\tCincinnati Bengals  \tJeremy Hill\n",
      "59\tBaltimore Ravens    \tJustin Forsett\n",
      "58\tCleveland Browns    \tIsaiah Crowell\n",
      "# -------------------------------------------------- TE ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "67\tNew Orleans Saints  \tJimmy Graham\n",
      "46\tChicago Bears       \tMartellus Bennett\n",
      "44\tCarolina Panthers   \tGreg Olsen\n",
      "33\tDallas Cowboys      \tJason Witten\n",
      "31\tNew York Giants     \tLarry Donnell\n",
      "26\tKansas City Chiefs  \tTravis Kelce\n",
      "22\tSeattle Seahawks    \tLuke Willson\n",
      "18\tPhiladelphia Eagles \tZach Ertz\n",
      "17\tPittsburgh Steelers \tHeath Miller\n",
      "16\tMinnesota Vikings   \tRhett Ellison\n",
      "# -------------------------------------------------- WR ---\n",
      "Points\tTeam\t\t\tPlayer\n",
      "117\tPittsburgh Steelers \tAntonio Brown\n",
      "96\tDallas Cowboys      \tDez Bryant\n",
      "87\tGreen Bay Packers   \tJordy Nelson\n",
      "84\tGreen Bay Packers   \tRandall Cobb\n",
      "77\tDenver Broncos      \tDemaryius Thomas\n",
      "73\tBaltimore Ravens    \tTorrey Smith\n",
      "66\tMiami Dolphins      \tMike Wallace\n",
      "64\tDenver Broncos      \tEmmanuel Sanders\n",
      "62\tCarolina Panthers   \tKelvin Benjamin\n",
      "52\tSan Diego Chargers  \tEddie Royal\n"
     ]
    }
   ],
   "source": [
    "## Take a look at what we made:\n",
    "#    the top 10 people in each position.\n",
    "#\n",
    "for position, points in stats.sort(columns='FantasyPtsTotal', ascending=False)[\n",
    "        ['Player', 'pos', 'Team', 'FantasyPtsTotal']].groupby('pos'):\n",
    "    print(\" \".join(('#', '-' * 50, position, '-' * 3)))\n",
    "    print(\"Points\\tTeam\\t\\t\\tPlayer\")\n",
    "    for row in points.head(10).apply(\n",
    "            lambda x: '{}\\t{:<20}\\t{}'.format(int(x.FantasyPtsTotal), x.Team, x.Player),\n",
    "            axis=1):\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up, convert to 2015\n",
    "\n",
    "The stats are for the previous season. Use the roster data ('`data/nfl_rosters2015.csv`') we have to map the player to the correct team, and add the bye week.\n",
    "\n",
    "\n",
    "- Rename the player 'TOTAL' to the player 'Defense' because that's what it is.\n",
    "- Read in '`data/nfl_rosters2015.csv`' and merge it with `stats` to correctly map players to their 2015 team.\n",
    "\n",
    "After we merge, we will have pairs of columns that need to be reconciled:\n",
    "- '`Team`' and '`Team2014`' -- the player 'Defense' doesn't actually exist so after the merge the '`Team`' will be null for 'Defense'...make sure the '`Team`' column is populated with the 2014 value\n",
    "- '`Pos`' (for 2015) and '`pos`' (for 2014). <br/>\n",
    "  Take the 2015 value if it exists, otherwise the 2014 value\n",
    "\n",
    "And then add the Bye Week for each player\n",
    "- Use the game schedule in '`nfl_season2015.csv`' to add a column `Bye` to indicate the player's bye week\n",
    "- Read in '`data/nfl_top_100s.csv`' and add a column indicating the player's rank in the 2015 list\n",
    "  (it is the [NFL one the players vote in][nfl100]. [ESPN also has one][espn100].)\n",
    "\n",
    "\n",
    "[nfl100]: https://en.wikipedia.org/wiki/NFL_Top_100_Players_of_2015\n",
    "[espn100]: http://www.cincyjungle.com/2015/9/1/9240767/espn-releases-top-100-nfl-players-list-2015-season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2177, 47)\n",
      "Total players with fantasy numbers: 196\n",
      "Total players on 2015 rosters with fantasy numbers: 137\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Player</th>\n",
       "      <th>FantasyPtsTotal</th>\n",
       "      <th>Top100Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Aaron Rodgers</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>New Englan...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Denver Bro...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Peyton Man...</td>\n",
       "      <td>132</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Indianapol...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Andrew Luck</td>\n",
       "      <td>148</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Pittsburgh...</td>\n",
       "      <td>WR</td>\n",
       "      <td>Antonio Brown</td>\n",
       "      <td>117</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team Pos         Player  FantasyPtsTotal  Top100Rank\n",
       "36   Green Bay ...  QB  Aaron Rodgers            165             2\n",
       "180  New Englan...  QB      Tom Brady            124             3\n",
       "144  Denver Bro...  QB  Peyton Man...            132             5\n",
       "112  Indianapol...  QB    Andrew Luck            148             7\n",
       "96   Pittsburgh...  WR  Antonio Brown            117             8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the player 'TOTAL' to the player 'Defense' because that's what it is.\n",
    "stats.ix[stats.Player == 'TOTAL', 'Player'] = 'Defense'\n",
    "\n",
    "# Read in 'data/nfl_rosters2015.csv' and merge it with stats\n",
    "stats.columns = ['Team2014' if c == 'Team' else c for c in stats.columns]\n",
    "rosters2015 = pd.read_csv(os.path.join('data', 'nfl_rosters2015.csv'))\n",
    "rosters2015.head()\n",
    "full_stats = stats.merge(rosters2015, on='Player', how='outer')\n",
    "\n",
    "print(full_stats.shape)\n",
    "print('Total players with fantasy numbers:', full_stats.FantasyPtsTotal.count())\n",
    "print('Total players on 2015 rosters with fantasy numbers:',\n",
    "      ((full_stats.Team.notnull()) & (full_stats.FantasyPtsTotal.notnull())).sum())\n",
    "\n",
    "\n",
    "\n",
    "# Reconcile 'Team' and 'Team2014' for the player 'Defense'\n",
    "full_stats.ix[full_stats.Player == 'Defense', 'Team'\n",
    "             ] = full_stats[full_stats.Player == 'Defense'].Team2014 \n",
    "\n",
    "# Reconcile 'Pos' (for 2015) and 'pos' (for 2014). \n",
    "full_stats.ix[full_stats.Pos.isnull(), 'Pos'] = full_stats[full_stats.Pos.isnull()].pos\n",
    "del full_stats['pos']\n",
    "\n",
    "# Read in 'nfl_top_100s.csv' and add a column indicating the player's rank\n",
    "# in the 2015 list that the NFL players vote on.\n",
    "nfl100 = pd.read_csv(os.path.join('data', 'nfl_top_100s.csv'))\n",
    "nfl100 = nfl100[nfl100.year == 2015]\n",
    "nfl100.columns = nfl100.columns.str.capitalize()\n",
    "nfl100.columns = ['Top100Rank' if c == 'Rank' else c for c in nfl100.columns]\n",
    "full_stats = full_stats.merge(\n",
    "    nfl100[['Player', 'Team', 'Top100Rank']],\n",
    "    on=['Player', 'Team'],\n",
    "    how='outer')\n",
    "\n",
    "\n",
    "# Peek at the data\n",
    "full_stats[full_stats.FantasyPtsTotal.notnull()][\n",
    "    ['Team', 'Pos', 'Player', 'FantasyPtsTotal', 'Top100Rank']\n",
    "].sort('Top100Rank').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------ week 4 \n",
      "New England Patriots\n",
      "Tennessee Titans\n",
      "------------------ week 5 \n",
      "Miami Dolphins\n",
      "New York Jets\n",
      "Carolina Panthers\n",
      "Minnesota Vikings\n",
      "------------------ week 6 \n",
      "St. Louis Rams\n",
      "Tampa Bay Buccaneers\n",
      "Dallas Cowboys\n",
      "Oakland Raiders\n",
      "------------------ week 7 \n",
      "Chicago Bears\n",
      "Denver Broncos\n",
      "Green Bay Packers\n",
      "Cincinnati Bengals\n",
      "------------------ week 8 \n",
      "Buffalo Bills\n",
      "Philadelphia Eagles\n",
      "Washington Redskins\n",
      "Jacksonville Jaguars\n",
      "------------------ week 9 \n",
      "Baltimore Ravens\n",
      "Detroit Lions\n",
      "Kansas City Chiefs\n",
      "Seattle Seahawks\n",
      "Arizona Cardinals\n",
      "Houston Texans\n",
      "------------------ week 10 \n",
      "San Diego Chargers\n",
      "San Francisco 49ers\n",
      "Indianapolis Colts\n",
      "Atlanta Falcons\n",
      "------------------ week 11 \n",
      "New Orleans Saints\n",
      "Pittsburgh Steelers\n",
      "New York Giants\n",
      "Cleveland Browns\n"
     ]
    }
   ],
   "source": [
    "##\n",
    "# Show teams by bye week\n",
    "season2015 = pd.read_csv(\"data/nfl_season2015.csv\")\n",
    "all_teams = set(season2015.homeTeam.unique())\n",
    "byes = dict(ByeWeek=[], Team=[])\n",
    "for wk, dataset in season2015.groupby('week'):\n",
    "    bye_teams = all_teams.difference(dataset.homeTeam).difference(dataset.awayTeam)\n",
    "    byes['ByeWeek'].extend([wk] * len(bye_teams))\n",
    "    byes['Team'].extend(bye_teams)\n",
    "    \n",
    "byes = pd.DataFrame(byes)\n",
    "\n",
    "for wk, dat in byes.groupby('ByeWeek'):\n",
    "    print('------------------ week {} '.format(wk))\n",
    "    print(\"\\n\".join(dat.Team))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Player</th>\n",
       "      <th>ByeWeek</th>\n",
       "      <th>FantasyPtsTotal</th>\n",
       "      <th>Top100Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Green Bay ...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Aaron Rodgers</td>\n",
       "      <td>7</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>New Englan...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Denver Bro...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Peyton Man...</td>\n",
       "      <td>7</td>\n",
       "      <td>132</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Indianapol...</td>\n",
       "      <td>QB</td>\n",
       "      <td>Andrew Luck</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Pittsburgh...</td>\n",
       "      <td>WR</td>\n",
       "      <td>Antonio Brown</td>\n",
       "      <td>11</td>\n",
       "      <td>117</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team Pos         Player  ByeWeek  FantasyPtsTotal  Top100Rank\n",
       "36   Green Bay ...  QB  Aaron Rodgers        7            165             2\n",
       "180  New Englan...  QB      Tom Brady        4            124             3\n",
       "144  Denver Bro...  QB  Peyton Man...        7            132             5\n",
       "112  Indianapol...  QB    Andrew Luck       10            148             7\n",
       "96   Pittsburgh...  WR  Antonio Brown       11            117             8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the player's 2015 bye week\n",
    "if 'ByeWeek' in full_stats:      # This part is in case someone runs the cell twice\n",
    "    del full_stats['ByeWeek']\n",
    "    \n",
    "    \n",
    "full_stats = full_stats.merge(byes, on='Team', how='left')\n",
    "full_stats[full_stats.FantasyPtsTotal.notnull()][\n",
    "    ['Team', 'Pos', 'Player', 'ByeWeek', 'FantasyPtsTotal', 'Top100Rank']\n",
    "].sort('Top100Rank').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove players we can't use\n",
    "\n",
    "The player's 2015 position is '`Pos`'. Delete players who can't be on the fantasy roster.\n",
    "\n",
    "1. Look at the positions listed\n",
    "2. Set the `Team` of the defense rows to be the same as in 2014\n",
    "\n",
    "\n",
    "First, let's add a value 'DEFENSE' as the position when the player is named 'TOTAL'. Then we'll delete all of the players who aren't the kinds we can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DEFENSE', 'K', 'RB', 'WR', 'QB', 'TE', 'RB/WR', 'WR-KR', 'FB',\n",
       "       'SP', 'ILB', 'HB', 'DE', 'CB', 'LB', 'DT', 'S', 'FS', 'LS', 'T',\n",
       "       'G', 'OL', 'DB', 'P', 'C', 'SS', 'OT', 'OG', 'OLB', 'TE/FB', 'C/G',\n",
       "       'NT', 'DL', 'WR/RB', 'T/G', 'RB/RS', 'G/T', 'SAF', 'MLB', 'FB/LB',\n",
       "       'WR/KR', 'RB/FB', 'G/C', 'CB/RS', 'H-B', 'G / T', 'TE/LS', 'C-G',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the unique positions\n",
    "full_stats.Pos.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WR         249\n",
       "TE         146\n",
       "RB         140\n",
       "QB          91\n",
       "K           34\n",
       "DEFENSE     32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change anything that starts with an 'WR' to 'WR' and anything that starts with 'RB' to 'RB'\n",
    "full_stats.ix[full_stats.Pos.notnull() & full_stats.Pos.str.startswith('WR'), 'Pos'] = 'WR'\n",
    "full_stats.ix[full_stats.Pos.notnull() & full_stats.Pos.str.startswith('RB'), 'Pos'] = 'RB'\n",
    "\n",
    "# And then keep only the players we will use\n",
    "full_stats = full_stats[full_stats.Pos.isin(('QB', 'RB', 'WR', 'TE', 'K', 'DEFENSE'))]\n",
    "\n",
    "# And drop people who aren't on a team in 2015\n",
    "full_stats = full_stats[full_stats.Team.notnull()]\n",
    "\n",
    "full_stats.Pos.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# -------------------------------------------------- DEFENSE ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "282\tPhiladelphia Eagles \t8\t--\tDefense\n",
      "231\tArizona Cardinals   \t9\t--\tDefense\n",
      "231\tIndianapolis Colts  \t10\t--\tDefense\n",
      "230\tBaltimore Ravens    \t9\t--\tDefense\n",
      "229\tGreen Bay Packers   \t7\t--\tDefense\n",
      "226\tSt. Louis Rams      \t6\t--\tDefense\n",
      "221\tCarolina Panthers   \t5\t--\tDefense\n",
      "217\tCleveland Browns    \t11\t--\tDefense\n",
      "215\tDetroit Lions       \t9\t--\tDefense\n",
      "214\tPittsburgh Steelers \t11\t--\tDefense\n",
      "# -------------------------------------------------- K ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "146\tIndianapolis Colts  \t10\t98\tAdam Vinatieri\n",
      "137\tGreen Bay Packers   \t7\t--\tMason Crosby\n",
      "136\tBaltimore Ravens    \t9\t--\tJustin Tucker\n",
      "132\tPittsburgh Steelers \t11\t--\tShaun Suisham\n",
      "127\tAtlanta Falcons     \t10\t--\tMatt Bryant\n",
      "126\tCarolina Panthers   \t5\t--\tGraham Gano\n",
      "119\tNew York Jets       \t5\t--\tNick Folk\n",
      "118\tCincinnati Bengals  \t7\t--\tMike Nugent\n",
      "116\tNew York Giants     \t11\t--\tJosh Brown\n",
      "108\tSan Francisco 49ers \t10\t--\tPhil Dawson\n",
      "# -------------------------------------------------- QB ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "165\tGreen Bay Packers   \t7\t2\tAaron Rodgers\n",
      "148\tIndianapolis Colts  \t10\t7\tAndrew Luck\n",
      "132\tDenver Broncos      \t7\t5\tPeyton Manning\n",
      "124\tNew England Patriots\t4\t3\tTom Brady\n",
      "120\tSeattle Seahawks    \t9\t22\tRussell Wilson\n",
      "118\tDallas Cowboys      \t6\t34\tTony Romo\n",
      "115\tPittsburgh Steelers \t11\t26\tBen Roethlisberger\n",
      "113\tMiami Dolphins      \t5\t--\tRyan Tannehill\n",
      "109\tNew Orleans Saints  \t11\t30\tDrew Brees\n",
      "108\tCincinnati Bengals  \t7\t--\tAndy Dalton\n",
      "# -------------------------------------------------- RB ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "111\tSeattle Seahawks    \t9\t9\tMarshawn Lynch\n",
      "90\tGreen Bay Packers   \t7\t60\tEddie Lacy\n",
      "82\tKansas City Chiefs  \t9\t12\tJamaal Charles\n",
      "77\tPhiladelphia Eagles \t8\t--\tDeMarco Murray\n",
      "76\tChicago Bears       \t7\t48\tMatt Forte\n",
      "66\tMinnesota Vikings   \t5\t--\tMatt Asiata\n",
      "62\tMiami Dolphins      \t5\t--\tLamar Miller\n",
      "59\tBaltimore Ravens    \t9\t65\tJustin Forsett\n",
      "58\tCleveland Browns    \t11\t--\tIsaiah Crowell\n",
      "54\tKansas City Chiefs  \t9\t--\tKnile Davis\n",
      "# -------------------------------------------------- TE ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "67\tSeattle Seahawks    \t9\t--\tJimmy Graham\n",
      "46\tChicago Bears       \t7\t--\tMartellus Bennett\n",
      "44\tCarolina Panthers   \t5\t89\tGreg Olsen\n",
      "33\tDallas Cowboys      \t6\t93\tJason Witten\n",
      "31\tNew York Giants     \t11\t--\tLarry Donnell\n",
      "26\tKansas City Chiefs  \t9\t--\tTravis Kelce\n",
      "22\tSeattle Seahawks    \t9\t--\tLuke Willson\n",
      "18\tPhiladelphia Eagles \t8\t--\tZach Ertz\n",
      "17\tPittsburgh Steelers \t11\t--\tHeath Miller\n",
      "16\tMinnesota Vikings   \t5\t--\tRhett Ellison\n",
      "# -------------------------------------------------- WR ---\n",
      "Points\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "117\tPittsburgh Steelers \t11\t8\tAntonio Brown\n",
      "96\tDallas Cowboys      \t6\t15\tDez Bryant\n",
      "87\tGreen Bay Packers   \t7\t18\tJordy Nelson\n",
      "84\tGreen Bay Packers   \t7\t100\tRandall Cobb\n",
      "77\tDenver Broncos      \t7\t20\tDemaryius Thomas\n",
      "73\tSan Francisco 49ers \t10\t--\tTorrey Smith\n",
      "66\tMinnesota Vikings   \t5\t--\tMike Wallace\n",
      "64\tDenver Broncos      \t7\t95\tEmmanuel Sanders\n",
      "62\tCarolina Panthers   \t5\t--\tKelvin Benjamin\n",
      "52\tChicago Bears       \t7\t--\tEddie Royal\n"
     ]
    }
   ],
   "source": [
    "## Take a look at what we made:\n",
    "#    the top 10 people in each position.\n",
    "#\n",
    "for position, points in full_stats.sort(columns='FantasyPtsTotal', ascending=False)[\n",
    "        ['Player', 'Pos', 'Team', 'ByeWeek', 'Top100Rank', 'FantasyPtsTotal']].groupby('Pos'):\n",
    "    print(\" \".join(('#', '-' * 50, position, '-' * 3)))\n",
    "    print(\"Points\\tTeam\\t\\t\\tBye   NFL100\\tPlayer\")\n",
    "    for row in points.head(10).apply(\n",
    "            lambda x: '{}\\t{:<20}\\t{}\\t{}\\t{}'.format(\n",
    "                int(x.FantasyPtsTotal),\n",
    "                x.Team,\n",
    "                int(x.ByeWeek),\n",
    "                '--' if np.isnan(x.Top100Rank) else int(x.Top100Rank),\n",
    "                x.Player),\n",
    "            axis=1):\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Picking strategy\n",
    "\n",
    "The obvious advice is don't pick a backup player that's got the same bye week as your primary one.\n",
    "\n",
    "Next, picking order could be\n",
    "- by straight points (the highest rated player in one of your open slots)\n",
    "- or by point differential (the biggest drop between this player \n",
    "The dataset we have now is the total Fantasy points (according to the standard rules) that the player earned in the regular 2014 season.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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   "source": [
    "# Add the difference in total points between each player and one below,\n",
    "# and each player and 10 below (worst case draft pick)\n",
    "full_stats = full_stats[full_stats.Team.notnull()]\n",
    "full_stats = full_stats.sort(['Pos', 'FantasyPtsTotal'])  # Don't forget to sort\n",
    "full_stats['FantasyPtsDelta'] = full_stats.groupby('Pos').FantasyPtsTotal.diff(1)\n",
    "full_stats['FantasyPtsDelta10'] = full_stats.groupby('Pos').FantasyPtsTotal.diff(10)\n",
    "full_stats['FantasyPtsBelowBest'] = full_stats.groupby('Pos').FantasyPtsTotal.apply(\n",
    "        lambda x: max(x) - x)\n",
    "\n",
    "full_stats = full_stats.sort(['Pos', 'FantasyPtsTotal'], ascending=['True', 'False'])\n",
    "full_stats.to_csv(os.path.join('excel_files', 'fantasy_points.xls'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some insight from the differences\n",
    "The script below prints out the top 15 players in each slot. \n",
    "- Last year's point totals are in the column **'Points'**.\n",
    "- **'(d1)'** shows the the difference in points between\n",
    "the player and the next best player.\n",
    "- **'(d10)'** shows the worst case points difference if you wait until the\n",
    "next round and everybody chooses that position.\n",
    "\n",
    "\n",
    "#### observations:\n",
    "\n",
    "- The highest points for a single slot last year is the Philadelphia Eagles' defense, at 282 fantasy points total for the season.\n",
    "    - If they perform like last year, everyone else will be down 51 points for the season -- which averages out to just about a field goal per game (the typical point differential for a win).\n",
    "    - The rest of the top 10 defenses were within 21 points of each other, then the dropoff gets a little steeper, so probably don't let anyone pick two defensive teams before you've gotten your first.\n",
    "- If you don't get the Seahawks' Jimmy Graham as Tight End it almost doesn't matter who you get.\n",
    "    - He's got 21 points over the next two players (Martellus Bennett (Bears) and Greg Olson (Panthers)).\n",
    "    - ...and after Bennett and Olson, the point differential between tight ends was almost nonexistent.\n",
    "- Quarterbacks and kickers have almost equal points, but Quarterback quality falls off faster\n",
    "    - Adam Vinatieri (Colts), Mason Crosby (Packers), and Justin Tucker (Ravens) were worth more than any Quarterback except for Aaron Rodgers and Andrew Luck last year.\n",
    "    - Look at the second set of plots below, it shows "
   ]
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  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ---------------------------------------------------------------------- DEFENSE ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "282\t51\t71\tPhiladelphia Eagles \t8\t--\tDefense\n",
      "231\t0\t21\tIndianapolis Colts  \t10\t--\tDefense\n",
      "231\t1\t28\tArizona Cardinals   \t9\t--\tDefense\n",
      "230\t1\t28\tBaltimore Ravens    \t9\t--\tDefense\n",
      "229\t3\t29\tGreen Bay Packers   \t7\t--\tDefense\n",
      "226\t5\t34\tSt. Louis Rams      \t6\t--\tDefense\n",
      "221\t4\t29\tCarolina Panthers   \t5\t--\tDefense\n",
      "217\t2\t30\tCleveland Browns    \t11\t--\tDefense\n",
      "215\t1\t28\tDetroit Lions       \t9\t--\tDefense\n",
      "214\t3\t30\tPittsburgh Steelers \t11\t--\tDefense\n",
      "211\t1\t35\tMiami Dolphins      \t5\t--\tDefense\n",
      "210\t7\t39\tSan Francisco 49ers \t10\t--\tDefense\n",
      "203\t1\t33\tDenver Broncos      \t7\t--\tDefense\n",
      "202\t2\t51\tKansas City Chiefs  \t9\t--\tDefense\n",
      "200\t8\t49\tMinnesota Vikings   \t5\t--\tDefense\n",
      "# ---------------------------------------------------------------------- K ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "146\t9\t39\tIndianapolis Colts  \t10\t98\tAdam Vinatieri\n",
      "137\t1\t31\tGreen Bay Packers   \t7\t--\tMason Crosby\n",
      "136\t4\t33\tBaltimore Ravens    \t9\t--\tJustin Tucker\n",
      "132\t5\t47\tPittsburgh Steelers \t11\t--\tShaun Suisham\n",
      "127\t1\t44\tAtlanta Falcons     \t10\t--\tMatt Bryant\n",
      "126\t7\t60\tCarolina Panthers   \t5\t--\tGraham Gano\n",
      "119\t1\t57\tNew York Jets       \t5\t--\tNick Folk\n",
      "118\t2\t63\tCincinnati Bengals  \t7\t--\tMike Nugent\n",
      "116\t8\t61\tNew York Giants     \t11\t--\tJosh Brown\n",
      "108\t1\t54\tSan Francisco 49ers \t10\t--\tPhil Dawson\n",
      "107\t1\t57\tMinnesota Vikings   \t5\t--\tBlair Walsh\n",
      "106\t3\t65\tSt. Louis Rams      \t6\t--\tGreg Zuerlein\n",
      "103\t18\t63\tWashington Redskins \t8\t--\tKai Forbath\n",
      "85\t2\t58\tOakland Raiders     \t6\t--\tSebastian Janikowski\n",
      "83\t17\t60\tPittsburgh Steelers \t11\t--\tJosh Scobee\n",
      "# ---------------------------------------------------------------------- QB ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "165\t17\t58\tGreen Bay Packers   \t7\t2\tAaron Rodgers\n",
      "148\t16\t42\tIndianapolis Colts  \t10\t7\tAndrew Luck\n",
      "132\t8\t31\tDenver Broncos      \t7\t5\tPeyton Manning\n",
      "124\t4\t27\tNew England Patriots\t4\t3\tTom Brady\n",
      "120\t2\t23\tSeattle Seahawks    \t9\t22\tRussell Wilson\n",
      "118\t3\t38\tDallas Cowboys      \t6\t34\tTony Romo\n",
      "115\t2\t37\tPittsburgh Steelers \t11\t26\tBen Roethlisberger\n",
      "113\t4\t36\tMiami Dolphins      \t5\t--\tRyan Tannehill\n",
      "109\t1\t35\tNew Orleans Saints  \t11\t30\tDrew Brees\n",
      "108\t1\t38\tCincinnati Bengals  \t7\t--\tAndy Dalton\n",
      "107\t1\t48\tBaltimore Ravens    \t9\t97\tJoe Flacco\n",
      "106\t5\t59\tAtlanta Falcons     \t10\t77\tMatt Ryan\n",
      "101\t4\t62\tNew York Giants     \t11\t--\tEli Manning\n",
      "97\t0\t61\tSan Diego Chargers  \t10\t43\tPhilip Rivers\n",
      "97\t17\t63\tChicago Bears       \t7\t--\tJay Cutler\n",
      "# ---------------------------------------------------------------------- RB ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "111\t21\t68\tSeattle Seahawks    \t9\t9\tMarshawn Lynch\n",
      "90\t8\t52\tGreen Bay Packers   \t7\t60\tEddie Lacy\n",
      "82\t5\t49\tKansas City Chiefs  \t9\t12\tJamaal Charles\n",
      "77\t1\t46\tPhiladelphia Eagles \t8\t--\tDeMarco Murray\n",
      "76\t10\t48\tChicago Bears       \t7\t48\tMatt Forte\n",
      "66\t4\t40\tMinnesota Vikings   \t5\t--\tMatt Asiata\n",
      "62\t3\t40\tMiami Dolphins      \t5\t--\tLamar Miller\n",
      "59\t1\t39\tBaltimore Ravens    \t9\t65\tJustin Forsett\n",
      "58\t4\t38\tCleveland Browns    \t11\t--\tIsaiah Crowell\n",
      "54\t11\t35\tKansas City Chiefs  \t9\t--\tKnile Davis\n",
      "43\t5\t26\tArizona Cardinals   \t9\t--\tStepfan Taylor\n",
      "38\t5\t28\tBuffalo Bills       \t8\t--\tLeSean McCoy\n",
      "33\t2\t24\tIndianapolis Colts  \t10\t--\tFrank Gore\n",
      "31\t3\t24\tArizona Cardinals   \t9\t--\tAndre Ellington\n",
      "28\t2\t30\tSt. Louis Rams      \t6\t--\tBenny Cunningham\n",
      "# ---------------------------------------------------------------------- TE ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "67\t21\t53\tSeattle Seahawks    \t9\t--\tJimmy Graham\n",
      "46\t2\t33\tChicago Bears       \t7\t--\tMartellus Bennett\n",
      "44\t11\t33\tCarolina Panthers   \t5\t89\tGreg Olsen\n",
      "33\t2\t--\tDallas Cowboys      \t6\t93\tJason Witten\n",
      "31\t5\t--\tNew York Giants     \t11\t--\tLarry Donnell\n",
      "26\t4\t--\tKansas City Chiefs  \t9\t--\tTravis Kelce\n",
      "22\t4\t--\tSeattle Seahawks    \t9\t--\tLuke Willson\n",
      "18\t1\t--\tPhiladelphia Eagles \t8\t--\tZach Ertz\n",
      "17\t1\t--\tPittsburgh Steelers \t11\t--\tHeath Miller\n",
      "16\t2\t--\tMinnesota Vikings   \t5\t--\tRhett Ellison\n",
      "14\t1\t--\tPittsburgh Steelers \t11\t--\tMatt Spaeth\n",
      "13\t2\t--\tSt. Louis Rams      \t6\t--\tCory Harkey\n",
      "11\t--\t--\tTampa Bay Buccaneers\t6\t--\tAustin Seferian-Jenkins\n",
      "--\t--\t--\tNew York Giants     \t11\t--\tJerome Cunningham\n",
      "--\t--\t--\tNew York Giants     \t11\t--\tDaniel Fells\n",
      "# ---------------------------------------------------------------------- WR ---\n",
      "Points\t(d1)\t(d10)\tTeam\t\t\tBye   NFL100\tPlayer\n",
      "117\t21\t68\tPittsburgh Steelers \t11\t8\tAntonio Brown\n",
      "96\t9\t47\tDallas Cowboys      \t6\t15\tDez Bryant\n",
      "87\t3\t40\tGreen Bay Packers   \t7\t18\tJordy Nelson\n",
      "84\t7\t43\tGreen Bay Packers   \t7\t100\tRandall Cobb\n",
      "77\t4\t36\tDenver Broncos      \t7\t20\tDemaryius Thomas\n",
      "73\t7\t35\tSan Francisco 49ers \t10\t--\tTorrey Smith\n",
      "66\t2\t30\tMinnesota Vikings   \t5\t--\tMike Wallace\n",
      "64\t2\t29\tDenver Broncos      \t7\t95\tEmmanuel Sanders\n",
      "62\t10\t31\tCarolina Panthers   \t5\t--\tKelvin Benjamin\n",
      "52\t3\t27\tChicago Bears       \t7\t--\tEddie Royal\n",
      "49\t0\t26\tDallas Cowboys      \t6\t--\tTerrance Williams\n",
      "49\t2\t27\tNew York Jets       \t5\t--\tBrandon Marshall\n",
      "47\t6\t25\tIndianapolis Colts  \t10\t35\tT.Y. Hilton\n",
      "41\t0\t21\tAtlanta Falcons     \t10\t--\tRoddy White\n",
      "41\t3\t21\tCincinnati Bengals  \t7\t--\tMohamed Sanu\n"
     ]
    }
   ],
   "source": [
    "for position, points in full_stats.sort(columns='FantasyPtsTotal', ascending=False)[\n",
    "        ['Player', 'Pos', 'Team', 'ByeWeek',\n",
    "         'Top100Rank',\n",
    "         'FantasyPtsTotal', 'FantasyPtsDelta', 'FantasyPtsDelta10']].groupby('Pos'):\n",
    "    print(\" \".join(('#', '-' * 70, position, '-' * 3)))\n",
    "    print(\"Points\\t(d1)\\t(d10)\\tTeam\\t\\t\\tBye   NFL100\\tPlayer\")\n",
    "    nrows = min(15, points.shape[0])\n",
    "    for row in points.head(nrows).apply(\n",
    "            lambda x: '{}\\t{}\\t{}\\t{:<20}\\t{}\\t{}\\t{}'.format(\n",
    "                '--' if np.isnan(x.FantasyPtsTotal) else int(x.FantasyPtsTotal),\n",
    "                '--' if np.isnan(x.FantasyPtsDelta) else int(x.FantasyPtsDelta),\n",
    "                '--' if np.isnan(x.FantasyPtsDelta10) else int(x.FantasyPtsDelta10),\n",
    "                x.Team,\n",
    "                int(x.ByeWeek),\n",
    "                '--' if np.isnan(x.Top100Rank) else int(x.Top100Rank),\n",
    "                x.Player),\n",
    "            axis=1):\n",
    "        print(row)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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    {
     "data": {
      "image/png": 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m/iQ29Xdnc70CgUA2NsYkdjs5AewFwPXAdT7/C5zB7Qyc4e9Bds3U+P/q7YEn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\nYFFsj6S9KE0n7jiOkxfyPYg1ADP7mOAg/w7sSpjJfAt4nNIwgmxt007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TABg/fjwDBgwo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TNev5GvkqSDzexN4A+ECeqeBFma9A/R2cBbFRiH4zg1jyHAb8xsFaUnl7m0qNcAuyXKZV6XRXlt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/4P/IXLUscdwPdxXoOM2HlJ/rFGPGjOlnZuVZK9SB5jiINTa3tzAze0DSywQFPyUu5xub+5fN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\n4ziO4ziOU3L4INZxHMdxHMcpOf4/8SkMpglwNkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10650f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 2, sharex=True)\n",
    "\n",
    "positions = ('DEFENSE', 'WR', 'QB', 'RB', 'K', 'TE')\n",
    "for (ax, position) in zip(axs.flatten(), positions):\n",
    "    p = full_stats[full_stats.Pos == position]\n",
    "    p = p[p.FantasyPtsTotal.notnull()].sort('FantasyPtsTotal', ascending=False).head(15)\n",
    "    p = p.sort('FantasyPtsTotal')\n",
    "    pos = [0.5 + r for r in range(p.shape[0])]\n",
    "    ax.barh(pos, p.FantasyPtsTotal, align='center')\n",
    "    ax.set_yticks(pos)\n",
    "    if position == 'DEFENSE':\n",
    "        ax.set_yticklabels(list(p.Team), size='x-small')\n",
    "    else:\n",
    "        ax.set_yticklabels(list(p.Player), size='x-small')\n",
    "    ax.set_title(position)\n",
    "    for s in ('top', 'right', 'bottom', 'left'):\n",
    "        ax.spines[s].set_visible(False)\n",
    "    if ax.is_last_row():\n",
    "        ax.set_xlabel('Fantasy Points, 2014')\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WwJu40QYPRksieEfjMjUARslqa0vcvWFCWCmoFfrIGscwSTcAzULfCb+TdHQ4\nboqnqp0madegzrAPMMl8C64H0FrSyaH9j/AVW6NsNPD2+CrIjZL+jH8xvJr9cRSmjguIUcKRyOZL\nkgFs0aJF7LrrrixatIgaNWrwn//8h5NPPpkFCxawZs0attnGFQ4l0bJlS666ysUMPvzwQyQV4LrX\nowDM7M3g73qK+e7OVbl2Cbe160UFlGb+EV5HhXpwu/ZQGN9bkpZL2tHMPizde2HquIBovyKR6kW1\nUDnI4RlK/LrOAxaY2UmStqa0lmIay1OfjpxdA6xMHaejaNMT4nRfibpCDWC6mR1a/tAx4BwzGyvp\nPDwN5LGSDsFzoXcNE9aplARYPI7rL+5PicyX8G26l9KdS2pNRjSwmb0jqT1wNHCDpJFmdlvZ4RWu\nZfiRSGRzoWHDhkyfPp0VK1Zw+OGH8+STT3LsscdyxRVXcPTRR9O/f3/mzJlDv379iq/Za6+9GDRo\nEAC33XYbX331VVEqSLYU5dilXFnALMpTfil1mzz1Rn67nedcYQWGFYlEqirVReUgzf64TxW4AsFH\n4fi0PO2/xFcw15dJlEQA9yZbh3Ye0DT4hiFpS0l7ZrSDYMDNo3t3kdQVf47PwpdGB3y1N2F0uH93\nSvyBnwMGBd83JLUJx1lfDgo+cF+b2X34CnMelYNIJFLVqFu3LsOGDePyyy8H4Msvv2THHXcE4J57\n7iluZ5ZvfsgkQjKW8KO4Xagvzy6tjUzlF3OlmW8ldQnt0kozokR14UTgpVR9Mr5WuMtUYvcjkUjk\ne7NBVmgl7UTJpOvzUBpJmo4btlW4fxW468EjkgbgSRESi53+BT8KuFPSRcARObfLtfAWghz+ggea\nvYm7Duwi6VTgM0rr0Lrzlhv8E4GbJdXHV3evxH1+k+cqCs90kKSLcT/bK/FV2uOBX0uag/upvV58\nA3c72A0XIn9bngb3TmAXYHqYyH6IB4NlrVwYnmXnL5JW46srZ5BJDKyIRNaVWrVq0a5dO7799lvq\n1KnDmWeeSf/+eaVbK410IFTnzp1p3rw5jzzyCBdddBF9+/bl/PPPp2HDhsXtclUOUsePAodLegu3\nWYn9eRa4W9LX+K4VeLrb1bitnIrHGJSZKZvZdEn5lF/64UozW1FWaebH8lS5SymdVfEjSa/j8QF5\nPtxovyKRyPqhcn7xr1+HbmGnArea2YhQ1x5oaWYP5bStYWZryvbyvcfQF2htZheF908Ad5nZU9+z\n3wl42txaeYvFAAAgAElEQVS5YQJ+lJllbvWV08d7YWwV2fJLX7fWz0pS5f5jRqoVlf23Xp3Ybrvt\nWLzYs7p+8MEHHHvssZx55pkMGDBgLVdWDmb2faL8K3Rhhl1sDow2T929yYn2a/Ml2o7IBqTSfsVu\nCJeD7sCyZDILYGYzk8mspBGSbpdnmPm9pC6SiiS9LulJSY1Cu3z1CyUNlot9T5H0kzzjUGi/Ba4i\n8Hl4f2a4boakkZJqSWohaXLxhdJhkkZnd1vMS0ALSfUljZc0LYzpsFQ/l0qaJWmmpFK6sZJ+JGmC\npCMlbSfpEUlTJb0ctgZzP6s/SPo/SXPD2B/PHpbFEktGiVSUpk2bMnToUIYPHw7AV199Rb9+/eja\ntStdunThhRdeAKCwsJDTTz+dAw88kF122YWxY8cycOBAWrduzSmnnFLc369+9Su6dOlCmzZtGDp0\naHH9tttuyznnnEO7du24/fbbueSSS4rPXX755dx2221MnDiRPn36FN/vtttK3OYbN/b4WUk7Spoc\n7MJMeYBtPnK/PLYIdmaupFHFjaQewZ7Nlie4Seo/lXRTaP+UpIJw73ck7Rva7Bvs2LRgw3cO9YcE\nezgjrAxnsKn/TmKJtiNSZalMyYTwK+43wA3lnL+HtUhlhfqJufXh+D3g9HA8BLgs4x79gI/xrbDF\nwFOpc41Sx0OBX4bjIkqkxO4lSHPl9DsBX+EAuAiP5q0F1At1PwFmhONewAvAFun7hvHvGJ6vV6gb\nCXQJx7sDr4bjEclnFd7PAnYLx1G2K5Z1KFgkP4l8VsLSpUutTp06ZmZ28cUX25gxY8zMbPHixday\nZUszcwmt7t2725o1a2zy5MlWt25dmzp1qpmZ7b///jZ9+nQzM1uyZImZma1atcr2228/W7RokZmZ\nSbKxY8eamdmXX35Z3O+aNWtszz33tCVLltiECROsd+/eZmZWWFhot956a5kxA+cDV/shNYA6Zpm2\nty8peS5cBeZbfPcssW/d8AWAhbgagoCngZ+HNhWRSawP1AjHPwPuCMdP4inMo/2qUiXajsgGpYyt\nWt+yIXxoLf1G0hhcd3CymSV+s2uTytoTD2rIJ6H1WHidhhvMrDHcay4aXhMYLekkMxsJdJB0FR5k\n1oCSaN8RwKnyRAgHkB2gJmCMpG+ABcA5+BfI9ZIOwP3S9girwt2Bu81sFYCZfZ7qYyxwqZmNDXXd\ngb1SW44NU8+RfFbgCSLulDQypz5FYeq4gCh7E4msO2YlZuy5557jmWee4eqrXflvxYoVfPzxx0ii\nV69eSKJNmzY0aNCALl08TqpNmzYsXLiQDh068OCDD/L3v/+d1atXs2jRIubNm0eTJk2oU6cOPXv2\nBKBevXrss88+jB8/nlq1atGmTRsaNWqUObZE9mbFihUUFhaCK7ucJOk7YIyZZckN5mO+mSVSh9Px\nSe7yUP8+QLA3B+J2tyIyidsAD8iDyWpQknxmMvBnSffigbJflh1OYeq4gGi/IpHqRVWT7XoLl6gC\nwDxF7MHA2ak25UplhS2z8iS0ElmrXGmuUt2E+6+WNA5XVhiJp3LsaWbzJQ2ixAiPAV4F3sYTL2T5\nqxpwvJVk/ULSaXiQQwczWyNpMZ44wcj2DTHcsPfC5cuSus557vl18YVmvw7bekcDr0tqa2bflG5e\nmPlhRCKRijNjxgxatWoF+OT26aefpmnTpmXa1a5dG4AaNWqw5ZZbFtfXqFGDNWvWsGDBAoYPH86r\nr75K/fr16dOnD99+6+arbt26pfo67bTTuOuuu9hyyy3p27dvmXvVqlWLNWvWFMve3HDDDRQWFlJY\nWHhLWDg4GviHpEus4vECZSQCocw+s1J1FZFJvBLfFbtLrrgwAsDM/ixpbBjnq5L2t3J1aCORSHWj\nqsl2jQcayhUFEupS1khCfqms3Pohkt6WR87uiIuDAxxXwTENBZI0i3WBTyTVpkTKCzNbDkwB/oS7\nHJRC0q+A7Sg7Sa0PfBwms0cB2+IyNmfgUcBvSTor8QEOXARsJenK8H4CcFbqXu3IQNJu5gkVLsO/\nSLapyMNHIpGK88EHH3DhhRdy9tn+G7xHjx7cfPPNxednzJhRoX7MjOXLl1OvXj3q16/PokWLiv1v\ns9hvv/0YM2YMDzzwAAMGDKBp06b079+fCRMmANCsWbPiez///PMsX74cgOCj+omZ3YEnOSjPh3Zt\nNMDdvgqCX+wdwP8B/6rIxZKeCX0slQfOnhbqCySNNbPZZnYNnoq8+fcYZyQSiZSi0ldozczkIt/D\nJBXiqVuXAlelm4W2mVJZYfU0qf8xPok9w8wekvQ+sCz0czwuQZPFqZK642kewWWywJcAXgc+Ad6g\n9ET7IaCTmc3KeK6/Sfo/yk7MRwJPh8n2JOA/oc0dYZwn4RJmDYBrU88/AHdf+A3uuvBXSf3x1d0n\ncH/Z4s8qcL2kFvik+tGyqxsQZW8ikXUnydi1cuVK6tSpw1lnncVpp7nX0eWXX865555L+/bt+e67\n7+jcuTP33ed5YZQtoVX8vl27drRq1YqWLVvSvHlzDjzwwLzta9euzW9/+1tWrFhBo0aN2G677Wjd\nunVxINhxxx3HiBEjaNeuHYcddlhxUBhwCHCBpFV48OsvynnUXPuV+/7nwAO47OF1+J7/k2b2RAWu\nNzM7MuwijcRjCm5KtdldLqO4Gl88eKXs8KL9ikQi60llOuRuiIIb2Icz6v+I69lOB4avpY/3gLo5\ndbvigWAz8QlkI3yy+w4lgV9tgAnhuBCX7ALPbjYPmJF1b8oGX7xCSd7zIbgxn00qeA4Pwrg69Pky\n0BVfuX0XODa0aYv7DU8PZbuc+1ossaxPiWweHHLIITZz5sziALBx48ZZ+/btrU2bNva73/2uuN02\n22xjZ599trVt29bwmIPZeJDqW/gu1FDczsygJPD1X8Ae4Vi4Xm1DK21DniAEq+bU98Pdsl7AbVU/\n3F7NBMbhqXAJ57bGJ7Rf4XbqcuBgPIjssXDfoRn32OR/B7FEOxLZ6FTafHFTZQpbF54HWkqaE+Ri\nOgOY2aXAUjPraGZnld9FJsNwrdz2uE/rdKAnnuDAMtpbqv5yoKOZdQB+X95NJO0C7EZJZrSbzKwr\nHvS2s6T9U/2/E/p8E7gBjxzuSYlj2UDgdjPrCOyLr3znGWYssVS0RDY1n3zyCXvssQe777477dq5\nx5GZMXDgQJ588klmzZrF/Pnzeewxj4f9/PPP6dWrF7NmzYKgVAD80cxaAScDHwY7sx9wkaRtcIWZ\nxBWsAJhlnvUrzV+BkZKek3R+jqtUK+AoXAnhVjzOoT2erKZXaJP8p7oEmBvs81X4BLoTbsPaAEdL\nKuuUvMn/FmKJdiRSVdnsJ7Tmvq0dgXPxAKnngyvB96WLmSVKAfcDS8xsH0qy6ZTHFNzo/wL4LuO8\ncJeHWfhqxJVmlkT6dg+6sjNwNYVWqeueDK+zgYlmtsrM3sZdLsBXbs+XZ+/Z0YKCQiQSqdpsv/32\nvP322/ztb38rrvviiy9o2bIlO++8M5I46aSTeOklzySbVkgIvG0l6gY9gP7yzIwv44ouuwAPAz+X\n+zqcSkasgJn9E5cOvB+fuL4W4g0AxpvZN2a2CJ9Ep+1V85yusnwHXjazxcFuvQk0W+sHE4lEIhVk\ng6S+rWzMbDW+1fWCpE+BY8L779Vt6jhtfL+jZKK/JaVJ2h2Jr3AcC/wWdw/I7TuRDesKjJL091B/\nI65o8LGk63PukVZvWJmqTxQb/hEmwz/DJ/Z9zGx66VsXpo4LiLI3kUjVxVLyYWaeTSyRvAmSXeC7\nNenMgwIGmtlLuf1JmoKvsh5InvSzZvYpPqG9X9Js3NXJyFE4SP2gXkPFFkdyFRUyrilMHRcQ7Vck\nUr2oarJdlYqkPYDVZvZuWFloS4km7WpVPH1u7orB65KON7NH8MCtRFvxP/iK8JuUVlFIMo8J2NnM\nXpRnF/uPJFn6myfV3symhMjfc4C/4V8MSyQ1wP2Db6jA2An33tXMFgA3hc+lFe4qkaKwot1FIpHN\nmAYNGvD222/z/vvvs9NOOzFq1CjOOOMMCgoKqFu3bvGEdsiQIa/mXPocMEjSZHP1lTb49v8aQmIb\nPCHC6tx7SioAXjOzr0NA7rbAItaunJBrX7/EFWDW5Rqi/YpEqjdVTbYrE0k7SXpU0r/lKV4flrR9\nBS6th68UvIlvbQHcEl7vBb6U9I9wjwyh7mLmS/oglPPxjGbnSJqJb60NCe1uwOW2vsZXMDqGyWPi\nKFQTFw2fCUwFhmRMZgFM0kXh+Npwv/FhzHPx7bqXyxmvZRyfKOnNsJW4AyUJJlIolljWsUQ2JTVq\n1GDQoEHF7z/66COGDBnCs88+yx133ME+++zD7rvvzh577MG5557LihUryigkUNpe3IkHZ00PK6xD\ngY8k1cFTdtcCzkvsrzz1d6KtvTcwLdi35/EYgV8Dh+I2rShoy+bavFLvzewz4A15qtvLyXa0zLCb\nm/pvIZZoRyJVlsqMMMtX8P/t04DTUnXdCGoCFbi+ZjnnJgB7VfJ4i/vE5bWeWM9+FqeOmwNTv8eY\nalSgzaaOCojlB1QilcO2225rnTt3ttWrV5uZ2bBhw6xdu3Y2ZMiQMm2bN29uX331VW51RezHM7jS\nwK64Jvc04Jhw7hTgznKuHQycZRvI3qbus8n/T8eyeZTID4pKsyEba4X2MOBLM7snqTCzSWY2R9Ju\nkv4laZqk1yS1B5DUL6zoTgAekrRLWB2YKemJnOhbwjWfhtcCSc9LekzSfElDU23+Jun1sNJ5fgXG\n/hLQQlJ9SePDOKdLOqy8e0n6I55gYrqk4fgf6haSRkiaK2lUakxdwrO9LunJ5NkkLZR0raQ3gEMl\nXS9pnqQZki7NHu4mt0Wx/CBKpLKQxIEHHsjEiRMBePzxxznuuOOSSR79+vXjmWeeKXXNsmXLOOSQ\nQ3jmmWeQtJ2kR8LO18uSOmTc5hV8l2gCng1xGK6AAO6D+4qkfcL104I92nkt4860pZKOlPRGsFMj\nQ11Fxsim/38dy6Yvkcj6sbEmtHvhSQyy+BDobmad8dXQ61Pn2gFHm1lv3M0gLbNVmNFX+q+hI9kS\nMX8wsy5AB+B4STvlGVeyh/IzPMnB1/iKRmdcSmtoqm3uvXaysrJiwn1e/2RmewE/ltRN0hahr2PD\nuB4HLk49z/tm1gn3lT3BzFqaS3vdQiQSqRaccMIJPPzww3z00UfUrl07nTQBSaVcDJYuXcrRRx/N\nhRdeyJFHHgmevOBaM9sb18D+a8YtXgFqmVkzXKf2Ydy9AGAf3P1pLtAt2Lgb8IyE5ZFrS5sEN4Zh\nwJHBTiW+FBUZYyQSiaw3GysorLyfXVsBt0pqi0e+Nk6dG2cu2wUus3VUOL4f30Irj5fNbDFA8L/d\nGfgA+KWkM3Bf2J1w/cZFOdcKz+L1DbAAD+iqgWfqOiCMcw9JyeeXe69mGX0CzDezeeF4Ou6GsBSf\nuE8IX1q1KAl6AxgdXr8AvpB0Dz7pfTr7sQtTxwXEKOFIZPNnv/324+yzz2bUqFH07t2bb775JrOd\nmXHQQQfRtWtXpkyZwpQpU8B/YO+VmvQ2zLh0KtBVUmPcFeprSbXlGRqbmtk8Sc3w+IBdcXu3JKOf\nNGlb2gT/wV4Xl/f6KIw30bntXoExEu1XJFK9qQ4qB29RWjEgzXnAAjM7SdLWeDBDQlqKJj0progH\neq5ETM1gqM8C9jWzLyWNxlPN5mLA8WaWBEog6TTcWHcwjxxenLq2zL0qOqbwLNPN7NA816wAMLPv\nJHXBNSb/DxdP71O2eWGebiKRyObMQQcdxJ/+9CfmzZvHgw8+mNlGEkcccQRAWuVgJS4FmFftxcyW\nSfoEV3RJVBFmA2fgqcABrgSeMrO7QuDXiHz95bGlW+K2M8s+29rG6BSWfzoSiVRpqoPKwTygg6SP\nVaJwcJSkS/AMMx+FdqeV08frko4Px2mZrVJIWkhZ/VhwI1sPWB4M8E74qkE+jpXrxCbUBz4GrgiT\n2cZ4YMWvyuljtaS1fcbzgKaSOoXxbylpzzKD98l+QzN7Gjgf3+aLRCKVQK1atejYsWNxeeCBBzb6\nGAYNGsR1111Ho0aNiv1ns7juuuv45ptvuOKKK5KqCcBZkgolDUriEBIktZL0L3wV9Xo8VS74xPY3\nuDsCuI37MBynbXFWGHt9oCme7TCxpRb6PFTSjuHeSazDBHwCnIypPZFIJFKJbPAVWvke0xN43u9u\nuJ7htni+73/iGbN+JWkALkOVWPJcD/HfAPdIugJfxe2bcbvyvMvNzGZJekvSvNBHGeHxchiJb/Pv\ngvua7Yz7oL0BvJfx3DVwia7ZkiYCf84zplWSTgRuDtt/NfGVkvk57esDT0hKJusXkUmUT4lE1pVG\njRoxffr0tTfcACTb8C1atKBFixbFdSorzVV87s4776R3794MGzYM3CXqr3jmQfCV1ZmpS27GZQl3\nAu7CfVzBMx42o0Q+8DrgXklXAc+S3xZjZjMlfQU8hQeZvRTqF0v6DfBMsP2z8Kxkv8HlEPvjO1tP\n5IwxebrMZ45EIpG1UpmSCVkF/+VelOdcX+D6cLwd8Aju6/UyvgLZAE/pmLRvhm/PA3QBivDtsieB\nRqH+PaBuOH4qnJ8N/NJK5LNm4kZ/Li4wnvR/FD6RnIob/uszxjwYGBSO64W+dw7vF+Kas2+E5/59\nOD8rdf878CA4wnNeFo5vwBMtHIzrPz4WxjI0nK8JPADMCf31yxjbpg5PjSWWMqUq0Lhx48z6bbfd\n1s4991xr1aqVHXXUUTZhwgTbf//9rUWLFvbKK6+YmdngwYPttNNOs27dulnz5s3tmWeesQEDBthe\ne+1lJ598cnFfAwcOtM6dO1vr1q3tL3/5S6l7XHDBBda2bVs77LDDimW5Dj74YHvzzTfNzGz27NlW\nUFBgZmbLly+3vn372t57722dO3c2SuzJYIJtstJ2YSYZUlu4usHL+E5TESV2rBC3fxOBd4ETQ30N\nPDnMW/iE9FVK5A1PwSfIMyixWc1x+/cP3NbWwyfKs0LpYdF+xVLBEqm2VN58szI7y7yB/zK/Ic+5\nvpRMaEfigV/gucRftZJJaddwfD6uAFALN7YNQ/3pwHVWdkKbTHK3xieCW+BG9lugZTg3AV853ipc\n2yT0/1LSZ86YC/Hgsul4oNYtqXPvAb8Ox3vjk+naQCPg33gyhFNCH1viXwhPhfav4m4MBcCn+AR/\nC3z1oynQGZiUutePMsZmYLHEshkVvo+h22jUqlXLOnToUFyKiorMzExS8fHhhx9uJ554opmZjRs3\nzo455hgz8wlt9+7dbc2aNTZ58mSrW7euTZ061czM9t9/f5s+fbqZmS1ZssTMzFatWmX77befLVq0\nqPge48ePNzOzU0891e6//34zMysoKLA5c+aYWekJ7cUXX2xjxowxM7PFixcb8JaZQf4J7Rl48OmT\neJKErUJ9fYK+Na7mckc4LsSTwNTEdWvfCfW9gcfDcRtgFa5g0woPXk36uhd3JWse2rQJ9ccDD6TG\nVT9nnJvB/9dYNs9C5f2xRzY3qKyyMYLCrILt8kXBjsaDn6bggWV9cWWC8pQBEn4n6ehw3BR3E1hN\nttrAcnw1+L8Akh4O7bOe51ozGy7PvPOipAPMbHJqvODbf2PMbCWwUtJ4fJI7KTxDV+BFYF9J9XDj\n/ml4niyFhjnAjpJuxRM9PJ/3k4xEIutEw4YNM10O6tWrx8EHHwxA27ZtadmyJQBt2rRh4cKFgLsA\n9OrVC0m0adOGBg0a0KVLl1LtOnTowIMPPsjf//53Vq9ezaJFi5g3bx5NmjShXr16HHqox4R27ty5\nuN98PPfcczzzzDNcffXVSVVdeZraTMzs75LG4WoIv8CDSg8GtiFb1cCAp81T4y6QlNjibnjaXMzs\nTUmzcB+BQ/HV3mnBftXBf8zPwW1qYptnATdK+jPwmJnlpuyNRCKR9WZjTGjLUzhIY2RHwT4BXCrp\nZqCOmf1bUjvKVwZA0iHA/vjq7kpJU/FV0RVkqw0YpSff5TlzCcBc+qYIFyhPJrSJMoPl9CG/xN4L\nmrgH45PberiG7WuptmXGZ2ZLg7RZL+C3knqY2YVlh1aYOi4gyt5EIuvPlluWxJfWqFGD2rVrFx+v\nXr26+Fy6PveaNWvWsGDBAoYPH86rr75K/fr16dOnD99++22Ze9SsWbO431q1arFmjZvDpC14UoUj\njzySBg0aADBjxoy+ZvZxPp9bADNbBNwp6W5gsaRtKV/VYGVWN3m6F55p7MpSlVJzUko1ZvZOCAY7\nGrhB0kgzu610V4Wp4wKi/YpEqhcbUrZrg6scmNkLwI8k9U3qQkKB1jlNJ5ARBWtmX+Db7tdRsvpZ\nEWWA+sBnYTLbAVhbVO08XFu2SdCXzZDEKo2kmvhK64KM05OA44LWYyPgEHyVGdynbQA+CZ4E/Da8\nlnMrbYtPbEfjAR55VA4KU6VgbY8QiUS+J2blb0KZGcuXL6devXrUr1+fRYsW8cILL6y132bNmhWv\nGj/66KPF9ccffzw1a9aksLAwke5amtlBQNLhwVYBtAC+C9eUp2qQxSTghNBna3yXzHD3hBMlbRPO\nbS/pJxnj2AH42szuwwPVMmxYIdF+RSLVl4KCgmLbVVhYSGVNZmHjyXYdi8tg/TtsoQ8CFodzybfB\nOUCBPF3iXHxbLGE0cGJ4JWzjJ8oAM/DtrbnyFLFNcMO7A1Bf0hzgEkq0FovvKelg4CfepX2L+/uO\nxwMl5pF/RWKopOmhzSozS75t0u0/C31Pw/19rwirKHfiARLLwmR9ErAjJSu8uSvFSV0ToCg8723k\nFWxULLFsRqVqsHTp0lKyXTfffDNAGaWB9PvkOFEkSLJ7SeK+++6jU6dOrFy5Ekm0a9eOVq1a0bJl\nS/r3789ee+3FNddcU+49li5dytVXX02XLl1YtWpVcf3ll1/OF198Qfv27WndujXA78KlBcDV8nTb\ncyVdF+p7AnOC7XgQODW4E1yHuwBMw3eFErtjQC9J3VLvAR4FPpH0FvBHgk011+v+IzBe0kxcDSaR\n62og6bxw3BaYEmznbyidbTF5+lhiySiRyNrR2lYXqgqSFpvZduG4KZ5N669mdmc51xTiWXNuyzhX\nI8P9IbdNAR6EUWY1N2y3jTZP9bixqB7/mJFIFWS77bZj8eLFPProo1xxxRUUFRWVSmGbpqioiNtu\nu43Ro0dnnl9HBCBpMG7PhocV2alAXzObXRk32QhE+xWJ/PCotF8sG2uFdqNiZh/gighnAUjaWtII\nSVMkvS6pu1wM/FfAHyS9Ial9aHO7pNdCfY+w2jFbUvFqgjyxAviqRPfQ5vSKjE1SUeJuIekUSbNC\n/xeEuuaSZoaxzJU0KnXt9ZLmhVXsSzP6jiWWKlGqK+PGjePiiy9m3LhxxZPZP//5z3Tt2pX27dsz\ndGjpRUkzo0WLFqxY4a6mX3/9Nc2bN2f16tUUFBQwd64nK2zcuDEXXngh7dq1o3v37sXtM0g+3Dq4\nSsoXAJK6yG3P65KeVEh4IGmhpMFyGzZFwVVAbn+ODMdDwrnZkm4ovpFfe7XcHr0sqaukCZLelXRs\naNNPIUFN6PMmSa9IelvSQaUGvhn8v4ylapVIJE21nNAGplOSEedSPPihK/BTXGprES5Gfq2ZdTKz\nmfgKQSMz2we4EdeMPQb3FdtT0s9z7nEJ8IKZdTSzuys4LgNMUhNcZucgXFP3Fwo+wbiKw5/MbC/g\nx3Kf422BE8yspZl1AG4pp/tYYtmMS/Vk2bJlnHTSSYwdO5YmTZoArkjw3//+lylTpvDGG28wduxY\n5syZU3yNJHr27MnTTz8NwNixYzniiCOoWbNmqS/sJUuW0LNnT2bNmkWTJk1K+dSmEP5DfDrwX1z/\n+31JWwB/AY41sy747tXF4RoDPjCzjniim/6p+uQf66ZgO9vhmcH2T7V5J9ijN3Et7R64i0NhxvgM\nt6/74YsJV2Q3iSWWipRIpDQbQ+VgU5H++dYDOFLSZeF9WuYm92femPC6Jy7v9T6ApJHAgXjCg6x7\nrOvY9gbGm9nS0P8YXBbnCcrKijXD01N+Ieke/Avp6eyuC1PHBcTAikhk47D11lvTqVMn7r///iRY\nq1hi66WXPCnh8uXLeeedd2jUqFHxdX369OGWW27hhBNOYMyYMZxxxhll+k5Le/3oRz/i7rvv5t//\n/jdQKkrYyJAUxFdp25Nf5jCxadNwPdpcust3kLYCtscnvkl2sSfD62zc3WEV8LZC6tsMHg+vbwDN\ny54uTB0XEO1XJFK92JAqB9V5QtsBlwwDn0AeFVwRismzZfF1nv5E5f4szO0r3X+ubFctM1stqQs+\nOf8/4GQylRgKK3GIkUikotSsWZNHH32UAw88kKZNm3LGGWdgZgwePJhTTz21VNu0Qe/WrRunn346\nS5Ys4bXXXmPkyJFl+k5Le7Vs2ZLGjRszePBgAAoLC4tSTQWkJQX3x7NzlSdzmNibNbiEYUln0lb4\nblXnENR6PS5/mHVtWuor34/9pE0il5hDYZ7LIpFIdaCgoICCgoLi9zn263tRLV0O5EFh1wO3hqrn\ngHNT5xO5mC9x6Zos5uMyXjtLqoFPIv+V06a868vDcAmvwyQ1lLQlnvb2JfJ8EUjaGs+M9jTuH5xH\ntisSiWwq6tWrx9ixY7nmmmsYO3YsPXr04K677uLrr/138sKFC1m2bFmpa2rUqEHPnj05++yzOfzw\nw6lR4/ubZZVICr5LxWQO87EVbq+WSGqA26lIJBLZ7KjUFVpJ3+FbT7Xw1dG+eAaw3czsJknHAHPN\n7J3Qvi8wNsmKlafPIuCsIA1THg2D71htfJV1uJndE85dhUt8zQxjmyYXGD8cz751AiU6jAbFKxwD\n8RznO+CT110kGWCS+uErH1uE+96S4UfbTtIHqX5/kTr3ShjXv/BJ7AgzmyFXR8hdvTV84vxEmPwC\nXCJtOKUAACAASURBVJT9MURH+UhkU5Ds+Oywww48/fTTHHHEETz22GP8/Oc/Z99992XNmjU0atSI\nMWPGlAlq6dOnDwUFBTz33HOl+mzbti3t2rUrlhU7//zzS90rHBdSIoP4B0n98VXUj4D9zOxRSbOB\nvwV/2pp4UoX5OY+Qdk78KXAYnj1MeOruhZS4GmRhGce5Do/J8T/J/P6J9isSiawflSrbpdLSWQ8A\n08zsxtT5EbiU1TPh/QTgbDObk9VfRduUc23NoLeYda6APJJbqTZb4CscnUJa2rrA9ma28PuMK/T9\nHtDazPKGK6farlVCLLSLnvKRKkd1kQ7cECRSYOURJrSf5soPhh/drbMzCpaPpE9x+zZK0hl4QNnR\na7tuHfqfgNvfuam6+B8hstkR7dMGp0rIdr0EtJDUVy431RVPeThMLhFzAh7dP0bSFCgjS3VJqq8z\nQt0bkvYKbQslDUoaBAOMpAJJL0p6BnhJUl1Jj0maI+luudTM1uGyBuHcfKVkuVLUxz/sZQBmtiJM\nZn+eMfZ8sjiZ9Wnk8l1TwjMODXXN5TI5/wDelFRP0rNyma9Zknpkf+ybOvI0lljWpUTWhzvuuIM9\n9tiDAw44AFwVBQBJRwV7NhUPMrVQX6QSucAj5DJbb0i6P/xwJ9XH7bjdS/RrJwO7hXN1wjUzJb0m\nlzusIektST8KberLpbtqSjozZdtGyrMwlsOm/v8YSyzpEqlKbJAJbTBaPYFZSZ2ZTcEjYs8JMlcP\n45lmjjezripflqpmqPsDniULyv5vS7/vBJxhZvsDZwPvmVlrYCSwc6ptJ2Ag0AY4Wu57W9Kh2RI8\nc9hCSfcqaCua2WM5Y8+UxQmfw9Dc+pzPqhUeWbxveMbGknqF0y2BPwb5riPwVZh2ZtYOd1mIRCLV\nmNwMZhMnTuTDDz/k+uuv5/XXX2fcuHHgP65NHsB1C3AosB8lsoUQvqElNQYuAA4xs07Ae3ga7pKG\nZr8mZbuBIylRRRgEfGFm7fFsX/eG3aOHCWlxgd7Ao2F37CEz6xps2/9SbSKRSKRSqWyVg8SPFTzd\n693ASTltcpeXk/dfkF+W6h8AZvacXJx7bUvUk83sf+F4P+BP4frxkpak2r2c+O/KU/I2A0opIZhZ\nP3kQWQ/gOkmdzCzRT0zGsSfZsjh74tqN+eRyhPup/T975x2mVXH98c+XKkgTwZogllipLtiwLIIk\niiiKqLGiURPBXzTWJEZd1BRjicGSGGNADVZQVOwIawQLvaigiYARRYRQFaXt+f1x5u7effd9dxdY\n3F2cz/PMs3PvnTt37stydt6Zc77nENyvF1wQfTLwHvChmSXtZ+JpKm8Bnjazt7O/ekGqnk+UvYlE\nai8tWrRg2rRppc7dfPPNNG/enDvuKM5xMBW3JfvgNuNTAElP4F/gE4Tbmg7A28HeNCS7BKDwHaht\ngBZ42lqAbsAtAGb2TlixbQoMAx4E/o4rsCRBuJ0k3QQ0A5oDFbhYFaTq+UT7FYlsXdQm2a7lQaC7\nmCx+UVmPzWy9KiVLVcx6Sq8wp6Vk0kazvGTQmfJYWVeszWw6MF3Sa7jhTia0ybvUIYssjqT22c5n\nIOB+M7sx49626fcws39L6oi7bdwhaXi2lL1R9iYS2bpp3749CxYsKNa6HTx48Kepy2n7ms3uCXje\nzCrKbGj4DtT7kv6Er+pelqtfM5snab2ko4HmqS/iDwDHmtkHwUWsbfmPLahgWJFIpDZT22W70sZv\nFf5NvcyxSstSnYErECT3ny9pgzyobDZu9RoSpKskHQM0yfH8NwkT42BsW6au7SzpstRxkhN9gKQv\ngp/YJ5KeCZPTjsDHWd4lkcVZFu5PZHGyyeUsSz3PcJeG0yQ9JGkPSTsopJ9MI2ln4Gszewj4Mzll\nuxRLLLWo1A7Wrl1bvO2/88478/3vf5/OnTtz+OGHf+tjOeiggxg7diwrV67kyy+/BDgetyVzcKnB\nXYO70/X4rlPC28COQHe5HOEnweaMDbapbfC9BdgJn8SCf7H+sTxd+HjcPiOPi/jKzFaFdsOAh4GH\nUs9sDHwhqUG4rwLHxOr+fYwllnSJ1CaqeoU2m7FKe1c/Btwv6WrcJ3QYMEzSStyPNJGlKgKWyPVf\nDQ9IWIuv3vYws9lyxYFnJc3EJ4VLsjwP3Od2eHApeAdPCfl1aPOZmd2ZZfwWxnYj7ht2IL6t9yaQ\nrGwUjz340Z6Gb+NNJ8jihFWJ03DJsKbhfKkvEWEF5LfAVXjGnjW43FkyxoT2wG2SNuB/YMqkE4rR\nmJHIlqFBgwbFW/+DBw+mdevWDBw4sML7ioqKSunKmtlG5aBPfGgTBgwYwKWXXspVV11F165dadWq\nFbiLEma2RtLPcXu4EldoSTJ2bYu7Ux2A+8w+h2f9GglcGu7NOgYzuza4al2D26n75RKIX1Mid0jo\n66/AI6lzBWF8X+A2NCfRfkUikc3CzGpcwTUV/4QHLoAb6BuAG8LxMKB3qA/GkxTMAu5I9TEfuBmY\njgdQHYQb1m/wIC2AAcCtWZ5/bub58MxLQ70XnpJ2FnB7etzhZ34Y84v4qskfM97tVtwndgzQOJwv\nBPYP9SU52lwW+puO6+xmjru6Q0JjiaVKS02koKDA7r77bps0aZIdddRRlpeXZ3369LGlS5eamdlu\nu+1mv/zlL61z58726quvWsuWLe2SSy6x9u3b25w5c+yiiy6yvLw8O+CAA+y2224r7nf77be3K6+8\n0tq3b289evSwr776yszMPvjgA8vPz7eOHTta165dbeXKlbZ+/Xq7/PLLrWvXrobbgzOsrD04HHgp\n1C8AfgqMC8f9gYesxFY2BtoCkyzDNlJ5e/swsBz/4n8QMA6fVCf2tj2eXndaKK0t2q9YanmJbDZV\nNnesyZnCngBODVvtaylZgYWSXyaAO83sIDzQoY2kw1Jt/o1PLvcCxobzP6bEUSvpozJMBfYJQRJ/\nA04Mz9xHLuOVycH4SsgBwMGSjgrntwdeNFcq+BQ4OctYWuZocx3Q2Txi+Jrsw6z2/9+xxFJFpeZi\nZlx55ZU8/fTTTJ48mb59+/L73/8eAEm0adOGqVOn0rNnT5YtW8Zxxx3HzJkz2WefffjDH/7A5MmT\nmT59OiNHjuTTT90FdunSpRx77LHMnDmTXXfdlaeeegqAs846i2uvvZbp06dTWFhIo0aNeOCBB9hl\nl12YOHEieODr1ZJaZgxzCr67BB4M9gZgYRfsEEqSJFT0Yaf/QXLZ2+b4rtsReODrHfgX/2MpsbcX\nAX8xj7M4BJ/85nhULLHUhhKpSVS1y0GVYWZvSbobDxAbgadgzEZPSVeG6zvgq6KJoX7WzJZLuhHY\nycyuBZB0X7i+MU4yyeR/H+ADM/tv6Gs4bsSfzmg/wcwWhDYj8NWS14EvzSyZXE/BV0UyydVmIu4+\n8SSuBJGFglQ9nxglHIlUPZKYMWMGRx/t8Z7r16+nXbt2xdf79y+JZ23UqBHHHnts8fEjjzzCAw88\nwIYNG1iwYAFz5sxh1113pUmTJsX95eXlMX/+fFatWsWyZcvo2bMnAI0bN6awsJA777yTxYsXc9tt\nt4Hv5NQFdsczewEk2Q7ny7W798PjD6YBefgX7rSva2XJZW+X4wlolsmzki02s3XAh5ISt4c3geuD\nROMTZjavbPcFqXo+0X5FIlsXtUnloKr5F649uy8hECFNWC39E5BnZosk3UpptYNExaAIX+UtvnUT\nxtIJn1yWGQbZv6plnksyfWUqK9TNcm+uNr1xC98X+AW+rZdBQZbuIpFIVVJUVETnzp0ZO3Zs1uuN\nGzfOWp87dy733nsvb7/9Nk2bNqV///6sWeP/3Rs2LDFddevWZcOGrEkOyc/PZ7/99uO+++7jiCOO\nAPhBOUN9C09ju9LMTNI7wFHA3pTWmq2QzbW3ZvZoeP4JwKuS+ptZaU2yaL8ika2a2q5ysDncA1xt\nZsvIPgndBp84LpXUHMi29b/ZSDoR3057FPgQjyRuE4LWTscn3pl0S0Ubn4xHB2/OGAS0CSu3V+Hb\nfTEMMxKpJj755BOmTvU4pzVr1vDBBx9UeM+qVato0qQJTZs2ZcGCBYwZM6bc9k2bNqVly5bF7b78\n8kvWr19Pr169uOeeeygq8u/JktoFe5TJW3hymYnh+G3cFWqm2UZHYW2WvZW0h5nNNQ/EfQVfNY5E\nIpEqYbNWaCVdj2d+KcK/nfc3s/mb2WcB0FIlCRqGhZ9lnFaCO8GDwPvAZ5S4GmQjfa8F39yLyD3R\nPEdSTzxYYjbQ08yS9LoXAc/gn9/LZvZMlme8g6/oNgeGmNkbQVu2eVBmWIAHjlVmddfwVdp/BrUE\nAYOz/0GKc9xIZEtRr149WrduTb169WjWrBkXXHABRUVFbNiwgeuvv5599tmnjIpB+rhjx47st99+\n7LvvvrRt2zZZYS3T7uGHH2avvfYqrl900UVcccUVNG7cmFdeeYULL7yQefPm0blzZ2bOnDkLt3/H\nUZZTcFeESQBm9t/gApBOwW1Z6pWxtzOAq4F7szw3W5+nSRoc7l+E7zA9Uvq2aL8ikcimoY3/kh5u\n9GCAm4FjzGxDMJKrzSyLo/9G9XsD7n+VzUgiqY55qsUai6R8PBp4IbBHCKJA0kjgMTN7UtIZwAGJ\nX28l+qxrnkqyvDbRSz2y1bKptqoqad26NYsXLwbgqaeeYvjw4YwcObJUmw0bNlC3bjZPospz3nnn\n0b9/f447LtsctQw5Z4FBu7sz8Gszez4Ejr0MbG9me1Sq8xy2J3xBf9LMulamn3DPPDxQdofMe6P9\nitRGaoJdquVU2bfYzXE52BFYkhg6M/ssmcxKuk/SZEnvSroiuUHSEkm3SpopaUzQks1GqReUVCjp\nT0H0+2xJP5U0UdJ0ScPDtn7S7g+SJkmaFYIhkNRU0sPyRAnTJXVLi4hLai9piqRpobQK568NY52h\nkIBBUq/QZpak23OMvw7u93tzxrvsS4nawjiCeoGkupJuT71TIlw+QNJTksYBj4dtxampcbYu++jq\njvqMJZYtUWoeK1asYLvtfKFz2LBhnHzyyXTv3p3TTjuNuXPncuSRR5KXl8fBBx/MjBkzitudeuqp\n9OrVix/84Afp9LUUFBSw77770qNHDxYtWlT8h/Lhhx+mQ4cOtG/fPgkCY/78+XTs2JEBAwYg6X1J\nj+UYpgFP4jtp4G4Cowh2SVIzSa+l7F+PcD5fnnDheeANSTtLmhDs0wxJSQRcfXk68lJjkFQQ7Nks\nSSUvWSHV/XsWSywbUyI1ik3V+8Izc80E3gPuxAMFkmvbhZ/1cDeAXcNxEXB0qD8InJWl3wJcAHwa\nLpWVh0/+bsnsP9RvJ2gwhnY3hvp5wN9D/Y/ATaEuPMNXW0o0F+8CLgj1hkB9fPtuDFA/eSbQCNdb\nbBP6GQ2clOUdrgHOwbP0TEqdfxS4KNQH4YEa4K4PV4R6I1xXsiWuBfkfoEm4NiRznBnPNbBYYtkK\nC1YTqFevnnXq1Mn23ntv23777e3DDz80M7OhQ4fannvuaatWrTIzs9WrV9uaNWvMzGzGjBl2zDHH\nFLfbd9997auvvrLly5fbjjvuaOvWrbOJEyda165dbe3atbZw4UJr0aKFPf/887ZgwQLbc889bdmy\nZfbNN9/YgQceaFOmTLF58+ZZgwYNbPbs2WZmie073KyMLRqKB5NOBBoAL+ABYfPC9Xop+7ITMD3U\n83Hlgp3C8RXAzaFeJ9iptrir2b6ZY0j9DRCuUnNYOJ5HhuZtaqw14Pcsllg2pvAtWJ2tHqqqbLIP\nrZl9Kakz0B3ogUetnmpmY4AzJP0E9/v8Hr4y+SmVk6wy4PeWcjkIvmVPptp0knQTPjFtjmfOSkjk\ns6YCZ4Z6D6BPGLcBK1Vas7GMnExYqfiHufQM5nI0nahAskvSrrgbRs+wJZfmCuBeST8Fnge+Cud7\nAQdIOiscNwP2CJ/Fy2b2ZTj/VuY4y358Bal6PlH2JhKpOlq0aFGcMWzkyJEMHDiQV199FYAf/ehH\nNGniGbi/+eYbLrnkEmbNmkXdunVZsqRERvuYY44pVj7YZZdd+Pzzz5kwYQInn3wy9evXZ6edduLo\no4/GzJg8eTI9evSgRYsWAJxyyikMHTqUunXr0qJFCx577DEGDx6cjy8AtCV3TMAruD1siNvihDrA\nrZK64Yoqeyc7Xrj04OehPhF4SNJ6YISZvRvs8gdmNie0SY+hPDnFcihI1fOJ9isS2bqosbJd5u4G\nY4AxkpYAJ0r6CBgIHGJmq+SaqYm0S2UkqyC7T0V60voAcKx5atlBlJ4YJ8/I7D+nn4ZlkZPJcY9V\nYpwdgf2Dr1g9oLWk0WZ2vJl9hktuIc+L/qNUPxeZ2RulOpcOIPXe2cZpUfYmEqkWevfuzTnnnFN8\n3KhRo+L6nXfeyR577MHw4cP56quvaNu2bfG1bPJcqqRgiZmx9957c8IJJzBhwgQKCgooKCgolNSb\n3PbU8AWBsbiBSD/sTHzFtJOZFUlajK/kQmnb80aIm+gDPCrp13i2sEybXkcVy3uVQ0HlmkUikVpJ\njZTtkrS3pD1DXXhaw4+BpvhK7Kowaeu5EX2ux9MzXht8cC9IX07VGwNfSGqA69NmTjQzGQNcHJ5R\nR1KzjOdmysnsH+45T1KD4E+2AneT6B78f5vjkl2vp/sysxfMbBcz2x1PpjDLzI4Pz9leJX+5rgWS\nBA+vAIMUZHdUIsGT6Uu8u0XZm0ikRvDmm2+y5557Zr22atUqdt55ZwCGDh1abj+SOPzwwxk1ahTr\n1q3j888/Z9y4cUjioIMO4rXXXmP58uWsWbOGp59+miOOOALfaCpFHeDG4Ae7UNIncqWY43D3gBH4\nJPPR0L61pPNxe70oTGaPxzMZ/jNjfJMlHQJ8gdu8h/Ev1feE60dJSoK7hE9ejS0spxiJRCJpNmeF\ntglwd2pyOBm4y8zWSJotaQ7ub5pedcy0wpnHy/BJ3mLgOWBUagKYblsQnvcF7lqQi+Sem4C/yuWy\nNuD+q5+lrp8m6UxgXRjz02a2WlJe6L8h8JGZHSjpGGA4LuX1iJVIdmVTYMhMutADuCm80rPAP8L5\n+3FpnWlhIptI8FjG/acFt4TicZZ95Sh7E4lsKZYvX07nzp0xM+rXr8/f/vY3wCel6VXWgQMH0q9f\nP+6//35OOumk4muZ7RLy8vI49thjad++PbvuuiuHHnooADvvvDM33HADRx55JGbGgAED6NSpE/Pn\nz8/spwi4zsweUkopRtJQ4GvgRnxXa0lwp6pnZv8I7kujg20cj8tpQWnbY3iq2vtx2/NDPO7gwnCt\nO26zwb26VqhycopZFiKi/YpEIptIVTrkbm7BjXD6OB+YFurb4pq0E/HJbM9wvgD4O75S+hFwWjj/\nGNA91ddY3B2gvH4eBCbgE/PMcTwZ6sInomeF42HAX3Dd2V/j/rDT8O2421N9LAFuxQPpxgCNw/m9\n8WCK6WFMTfEVlYdxncd3gI6hbfdw/3QyAirCdYslllhyl62UTDtwAzAoy/nXcRvyDu5ikM0G5xNs\nXercJDypC7iyTXE7PEZiISWBvB1w//9C3H49Q0mAWCHwh9DfLGB/i/Yrlq2wRDaK6g8K+5aYBuwT\n6tcCz5nZALms1huUbLnvDhyNqwq8DDyOG9v+wDhJOwA7m9kMSb8rp589gKPMbH2WsfQMW3g7AJ/j\nygTgv8DbmdnBkhrhK7dH4gb+OUknmdnTuGrBi2Z2VVi9OBnf2vsnrhGZyJitBS4DVphZR0kH4xPt\nTni621+Y2WvyBAtZsMp8rpHId5Dv/OrfJfikdriZTS+nXWLrEvZO1UsZGDNbIOmvpLTDJY0G7jaz\nEZKuxhcLLg33rjWzrpLOAy4H0m5lmd1HIrWQ77ydqTZq+oQ2/ZvRC+gt6TfhuLGkHXELONo8QG2u\npBbh+ovAH8MW/knAyEr080yOySzAGDPrDyBpCC7N9dtwbUT4uQ+5VRDKKDyESel25soQmNnqcF83\n4JZw7h1JjYJrxwTgljAhfhJYVXaYBal6PjFKOBLZutjUKGEzmyVpFiW++7kotnUACnrdFZC21V0s\nxA3gO03Pp65lU6FJUZCq5xPtVySydVFjVQ6+BTrhK57gBvN4M/sk3SD4ka3NvNHcB3YSvlraD7iq\nEv18XclxvQD8X+o4131pH9rKKjyk701jZnaLpBfwSOO3JR1mrpyQoqCCbiORSG1mM6OEi0LZkqSX\nWTPtWC4VmkDBFhlQJBKpGdRIlYMtjaTv4z6nd4dTr+DbVsn1TpXo5klcQmxXM5uxGf1kchjur5vJ\nB7iOY5uwMnw68K9cnZjZKjwSuGcYSxO5BuR4XL0BSQcBX5mrRuxpZrPM7Hd4wEXbTRh7JBKJVCWr\ncN//hMmS+oX6mWQowUQikciWYItNaOW6tEn9HHnK1uYV3NYiyM68h29N/cXMhoZrNwHN5WkX38P9\nrxIsR/1F4FhKqwGMAE6XtEbS18CTwfc1D895nqScTaeVNYJfmaQZQFc8rW3x9fBu9+KKCLPxLDtf\nACvkWryZzmEmaSxwNvCb0O+reEDYPeGzmIGvBm8r6VzgMnk64Rm4OPpbZT9CxRJLLFlLzaZevXp0\n7tyZ9u3bc/LJJ/Pll19WfFMgSAumk8+YpDxJj2ZzGZDUR9LgjHPb4YsAFo5PkbQqdb0nnvQFPH1u\nYtOeA34c7ONIPDPj/0n6DJcuLPWc9BizvEkssdTyEqk2qjLCLF0IigV48NO7QKst9ayNGFNjYC7Q\nK3WuL7BDRrtxwAEb2ffTQEHq+ADcmB9FRtTwRvS5E65jW9n21R7dGUsstaXUNFq1alVcP/vss+22\n226r7K2QRZ0gnG9LFkWUXAVPZZ4oGtyO+/snKivXAddayr5X0FeFbTLaV/vvRCyxVHf5DlJlc7wt\n6nIg6YfA74EfmtmScK5A0kRJsyTdkWo7X9IN4Vv+REk7hfOnS3pf0nRJo8K5QyS9KWmKpEJJbcL5\n7pJmhrbZAhnOAF43s1eSE2Y2ysy+COMaJKkv0AUYIWmSpKMlPZIa50/kmW/S7/kDoIOZFaT6fc/M\nkjSUzSU9LekDSben7kuvYl8T3nuGpCvC6ReBPcJn0lHSrZLmhPe7NvunXu3/H2OJpRaUmk23bt34\n6CP3apo7dy75+fl07NiRE088kWXLlgEwceJE2rVrR1AkOCW5V9JewXbNIPj6y5kdgkuR1FTSR8E1\nKs1buEsV+I7VvcCh4fgQUrtCwR7NlCeaaRzOFUo6QNJvKdlxS9QPzg42bnraDpamun8vYomlOktk\nc9iSE9pmeAKC48wsnTv8z2Z2EK5X2EaeThH8X/MTM+uMT+QSOZdfA33MrBO+PQ++inC4meUBdwCJ\nYkEia9UJl/HKZD9yJ2IwwMxsFK5P28/MuuKrtZ0kNQntzsK1ZzP7nUF2BByIy3y1A/rIM6glz0RS\nL9zP96DQ9jh52tu+wPvhM1kAnGpm+4b3uyvH8yKRSC1mw4YNvPTSS7Rr1w6An//851xyySXMmDGD\nbt26UVBQAMBPfvITHnroIYJ9aEXJX8Q7gd+aWUc8EYIvg8ATwKmhzSnAU1Y6EQz4hPXQ4MtfF/d/\nTSa0ebiGLXhGsRfNrAPu/nRyOJ/Y0WuB5WbW2cwGStoPzy52SLBfrSQdt7mfVSQSiSRsSZWDr/DJ\n49mUDl3tKelKYBtc0/VFSjLJJL6uU3DjBy5Vdb9cAiuRx2oJ/FPSHvikfGmqbQWyVpV2chG4ZZb0\nBJ6l6zWgqZm9l9G2oq9Wb5rZYgBJ7+J6uQtS1xMpsSPCcRPgB3gChYQVuD/uUNx/bXT2RxWk6vlE\n2ZtIpHaQZCFbsGABe+yxBz/72c8AmDx5MqNH+3/3s88+m6OOOopGjRqxcOFCnn32WfLy8vLxxYNz\nQldp2azheIZC8C/iD+KJaM4iFRyb4i08/XgnYIaZ/Uee5nwv4HMz+yq0KyNDWMHr9cBXeKfIFWUa\n4QsHGRSk6vlE+xWJbF3UVtmuDfi39jckfWJmD0jaBs8nnmdmi8LWfcPUPYmkSxFB0sXMLpbnEe+D\nR8+2x9M4Pmdmfw8rmcNC24pkrWYD3So5/vQkdRj+h2Dn8DOTOUAHSQorIZlUJNklYLCZPVTqpNS2\neDBm6yV1wSe/p+N/kPpThoIsj49EIjWdFi1aMG3aNFavXs0xxxzDs88+S9++fUuluDUzmjRpwq9+\n9StGjRpFQUEBBQUFhZJOSHWVtkFK3TtP0npJRwPNzezdLMOYjU9O8ylxL1gKHE/pFLabIkN4v5nd\nWH6zggq6iUQitZlaK9tlZl/i6WbvCNtL2+CG7bPgY3USvjJ5eK4+5FJVb+NuBWvxra6meI5wgPMy\n2pYnazUcOErSMal7fi4pmRgmxn8VJdG8mNl8YD2eu3yUpM8lNUxd/zfQGhgiVy5IlBLGUnb1dn+g\nfca5V4AL5GoLSGqb+Lqlxrkt0MLMRgNX4CsokUikhlGnTh0GDRpUfLxw4ULq1q3L4MGDy71vxYoV\nvP/++zRu3JghQ4Zw3XXXAdClSxdGjvS8MMOHD+eoo46iefPmNGzYkGnTihN6/ZgSWzNZ0omhfkbG\nY4bhyQ4yvzwXSBoUvpBPBS7GFwW64Tb8FuBESRdReTakfHRfw3e5Wobn7ZDESUQikUhVsCUntIlx\nXYqngf0rsBeew3s9rk/4JvB8KngqfW9y/62SZgIzcZ+vT3FZmD9JmoKvFCRty5W1MrOvcVeGa0KA\n1rv4pDrtxwtu9IdJmpi6/XFgmnlChkJ8xQIASfuH5zUFdgvj/SnwTUa/4BPtWenzZvYy7m7xtjyT\nz0OUrFwn9zbDU+lOx9P7Xk1WqluyJJZYakPZcrRs2ZJ33nmHoiJ3Tx0xYkQSvFXhvckGT15eHm3b\ntmXkyJEMGTKEu+66i44dOzJ+/HhuuOEGAO6//37OOeccJE0FlqS6uQy4NtiK+pS2PyOB7YBHHfk3\nmgAAIABJREFUKE26zVtAEzObBzyAqxvUw1dtx2Vpn+0YfDdrlqR7zex9PLPia8E+jw7jyKC6fy9i\niaU6S2Rz2GITWjPbIani+qrnmtlk3MD+DhhqZucAB0nqbWa7A+9LugHXeG0naSczOxlfMXgN97/9\nEGhgZvsAB+GrvgTj/ZaZtcO34w8EpoYo21YhyvdO4FF8lXcgHpi2J76K0ReYJ+lg4ErcB3i1goIC\nPul9INQfp/R2/6nAI2Y2AN/K6wD8Ck+I8DrwF7kqQ6vQbyJCvlpB2QFfYfmhmbXHA+JexH1lx0ua\nZGYLgV9S8m/26437F4lEImkklSlV1e8RRxzB66+/DsCoUaM4+eSTiyeruVQLunXrxsMPP0zXrl1p\n3749t9xyC/369WPRokWsXbuWevXqsWLFClauXAnACy+8QNeuXcFtyvH4l/C/Ac/iKbg7mdkVwHXB\n/kzFvzi/ZGZLJF0k6UNJE4B9k/Gb2WAz2zEctgRGmVldM/t32I0CuEjSO6HPY4E/h/Mfh2tv4YsF\nF4egsAFA5xAk1hG3Y4k/biQSiWw231amsCeAUyXtjLsNpFcT0quxuZQODNjOzA7FVz6vD+d/AnwW\n1AEOBa4OW1oX4kkZOuOBCCvw9Ld7hgljXzwwoj6++vBgMLQv4iuopRQUwkpuazN7Ljz3ReDI4BMM\nHjH8eGqsxUjqga9MHB+kyyrzvncCN4Zo4K9S7StScUh1H0sssWxcqTpOPfVUnnjiCRYuXEiDBg1o\n1apV8bVcqgUADRo0YNKkSVx++eXccYerGh5wwAGMHz+eKVOmcPnll3PzzSU5XT755BPw9N5n4oGw\nfzOzA3C5v07hS/SVQHfgGTwpzPuSdsHTgXcBfhh+ZvsQ7gX+I+mJILuV/M0oNLODzexA3GUq8bEw\nstvqzL5zfODV/TsQSyzVWSKbw7cyoTWzt/DV1NMpUSrIRVrpoG3q/Kjwc2rqfC/c93Qa7r7QDNgD\n3zK7QtI1wC5mtg4PBhsexvMx8CGwT+gnvTTTEng6bP3/HtjfzNqZWe/U+3yDrxj3ltQOWJdauUjT\nEbgN6G1mS7Ncz/W+B5rZs6H+eGp8iYrD/wHb5ugvEolUM4ceeigTJ07kscce45RTTil1bfLkycXn\nzj77bN54443iayeddBIABx54IPPnzwdg6dKlnHTSSbRv355f/epXzJ4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QAyXA62COLfyuZJTXkvNeV9AJjZxKrqa7PfocV3BP6SUfccnpf+FmCYpOuBF4AmklYBt+OLxTT9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X8A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\ndWg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t2rWjR48ef8LnlnF4qFLCM3j40p9Sbcl80hq4TdIaXFbw97nuZ2ZrgoThOFy55Vxg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\ngyAIgiAIgkITDdogCIIgCIKg0Px/iWTnwrWfs1oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x100573090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 2, sharex=True)\n",
    "\n",
    "for (ax, position) in zip(axs.flatten(), positions):\n",
    "    p = full_stats[full_stats.Pos == position]\n",
    "    p = p[p.FantasyPtsBelowBest.notnull()].sort('FantasyPtsTotal', ascending=False).head(20)\n",
    "    p = p.sort('FantasyPtsTotal')\n",
    "    pos = [0.5 + r for r in range(p.shape[0])]\n",
    "    ax.barh(pos, p.FantasyPtsBelowBest, align='center')\n",
    "    ax.set_yticks(pos)\n",
    "    if position == 'DEFENSE':\n",
    "        ax.set_yticklabels(list(p.Team), size='x-small')\n",
    "    else:\n",
    "        ax.set_yticklabels(list(p.Player), size='x-small')\n",
    "    ax.set_title(position)\n",
    "    for s in ('top', 'right', 'bottom', 'left'):\n",
    "        ax.spines[s].set_visible(False)\n",
    "    if ax.is_last_row():\n",
    "        ax.set_xlabel('Fantasy Points below Top Player, 2014')\n",
    "        \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
